Train 1D CNN

1D Convolution을 기본 구성 요소로 하는 EEG classifier를 학습해보는 노트북.

• Three-way SoftMax classifier of normal, non-vascular MCI, and non-vascular dementia

---

환경 구성

# for auto-reloading external modules
# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython
%load_ext autoreload
%autoreload 2

# Load some packages
import os
import glob
import json

import matplotlib.pyplot as plt
import pprint
from IPython.display import clear_output
from tqdm.auto import tqdm

import numpy as np
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import transforms
from typing import Type, Any, Callable, Union, List, Optional

# custom package
from utils.eeg_dataset import *

# Other settings
%matplotlib inline
%config InlineBackend.figure_format = 'retina' # cleaner text

plt.style.use('default') 
# ['Solarize_Light2', '_classic_test_patch', 'bmh', 'classic', 'dark_background', 'fast', 
#  'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn', 'seaborn-bright', 'seaborn-colorblind', 
#  'seaborn-dark', 'seaborn-dark-palette', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 
#  'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 
#  'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'tableau-colorblind10']

plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams["font.family"] = 'NanumGothic' # for Hangul in Windows

print('PyTorch version:', torch.__version__)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

if torch.cuda.is_available(): print('cuda is available.')
else: print('cuda is unavailable.')

PyTorch version: 1.9.0
cuda is available.

# Data file path
root_path = r'dataset_segment/'

meta_path = os.path.join(root_path, 'metadata_debug.json')
with open(meta_path, 'r') as json_file:
    metadata = json.load(json_file)

pprint.pprint(metadata[0])

{'age': 78,
 'birth': '1940-06-02',
 'dx1': 'mci_rf',
 'edfname': '00001809_261018',
 'events': [[0, 'Start Recording'], [0, 'New Montage - Montage 002']],
 'label': ['mci', 'mci_amnestic', 'mci_amnestic_rf'],
 'record': '2018-10-26T15:46:26',
 'serial': '00001'}

---

Data Filtering by Diagnosis

Non-Vascular Dementia, Non-Vascular MCI, Normal

diagnosis_filter = [
    # Normal
    {'type': 'Normal',
     'include': ['normal'], 
     'exclude': []},
    # Non-vascular MCI
    {'type': 'Non-vascular MCI',
     'include': ['mci'], 
     'exclude': ['mci_vascular']},
    # Non-vascular dementia
    {'type': 'Non-vascular dementia',
     'include': ['dementia'], 
     'exclude': ['vd']},
]

def generate_class_label(label):
    for c, f in enumerate(diagnosis_filter):
        inc = set(f['include']) & set(label) == set(f['include'])
        # inc = len(set(f['include']) & set(label)) > 0        
        exc = len(set(f['exclude']) & set(label)) == 0
        if  inc and exc:
            return (c, f['type'])
    return (-1, 'The others')

class_label_to_type = [d_f['type'] for d_f in diagnosis_filter]
print('class_label_to_type:', class_label_to_type)

class_label_to_type: ['Normal', 'Non-vascular MCI', 'Non-vascular dementia']

splitted_metadata = [[] for i in diagnosis_filter]

for m in metadata:
    c, n = generate_class_label(m['label'])
    if c >= 0:
        m['class_type'] = n
        m['class_label'] = c
        splitted_metadata[c].append(m)
        
for i, split in enumerate(splitted_metadata):
    if len(split) == 0:
        print(f'(Warning) Split group {i} has no data.')
    else:
        print(f'- There are {len(split):} data belonging to {split[0]["class_type"]}')

- There are 10692 data belonging to Normal
- There are 9375 data belonging to Non-vascular MCI
- There are 6326 data belonging to Non-vascular dementia

---

Configure the Train, Validation, and Test Splits

Split the filtered dataset and shuffle them

# random seed
random.seed(0)

# Train : Val : Test = 8 : 1 : 1
ratio1 = 0.8
ratio2 = 0.1

metadata_train = []
metadata_val = []
metadata_test = []

for split in splitted_metadata:
    random.shuffle(split)
    
    n1 = round(len(split) * ratio1)
    n2 = n1 + round(len(split) * ratio2)

    metadata_train.extend(split[:n1])
    metadata_val.extend(split[n1:n2])
    metadata_test.extend(split[n2:])

random.shuffle(metadata_train)
random.shuffle(metadata_val)
random.shuffle(metadata_test)

print('Train data size\t\t:', len(metadata_train))
print('Validation data size\t:', len(metadata_val))
print('Test data size\t\t:', len(metadata_test))

print('\n', '--- Recheck ---', '\n')
train_class_nums = np.zeros((len(class_label_to_type)), dtype=np.int32)
for m in metadata_train:
    train_class_nums[m['class_label']] += 1

val_class_nums = np.zeros((len(class_label_to_type)), dtype=np.int32)
for m in metadata_val:
    val_class_nums[m['class_label']] += 1

test_class_nums = np.zeros((len(class_label_to_type)), dtype=np.int32)
for m in metadata_test:
    test_class_nums[m['class_label']] += 1

print('Train data label distribution\t:', train_class_nums, train_class_nums.sum())
print('Val data label distribution\t:', val_class_nums, val_class_nums.sum())
print('Test data label distribution\t:', test_class_nums, test_class_nums.sum())

# random seed
random.seed()

# print([m['serial']  for m in metadata_train[:15]])
# print([m['serial']  for m in metadata_val[:15]])
# print([m['serial']  for m in metadata_test[:15]])

Train data size		: 21115
Validation data size	: 2640
Test data size		: 2638

 --- Recheck --- 

Train data label distribution	: [8554 7500 5061] 21115
Val data label distribution	: [1069  938  633] 2640
Test data label distribution	: [1069  937  632] 2638

print('=' * 50, 'TRAIN', '=' * 50)
for m in metadata_train:
    if m['edfname'] == '00649687_010715':
        print(m['serial'])

print('=' * 50, 'VALIDATION', '=' * 50)
for m in metadata_val:
    if m['edfname'] == '00649687_010715':
        print(m['serial'])

print('=' * 50, 'TEST', '=' * 50)
for m in metadata_test:
    if m['edfname'] == '00649687_010715':
        print(m['serial'])

================================================== TRAIN ==================================================
06880
06890
06888
06900
06878
06889
06902
06883
06893
06904
06892
06876
06885
06897
06895
06877
06899
06884
06896
================================================== VALIDATION ==================================================
06875
06887
06886
06891
06894
================================================== TEST ==================================================
06901
06881
06903
06898
06882
06879

Wrap the splitted data using PyTorch Dataset

ages = []
for m in metadata_train:
    ages.append(m['age'])

ages = np.array(ages)
age_mean = np.mean(ages)
age_std = np.std(ages)

print('Age mean and standard deviation:')
print(age_mean, age_std)

Age mean and standard deviation:
70.73961638645513 9.520262451914755

composed = transforms.Compose([EEGNormalizeAge(mean=age_mean, std=age_std),
                               EEGDropPhoticChannel(),
                               EEGRandomCrop(crop_length=round(200*15)), # 15s
                               EEGNormalizePerSignal(),
                               EEGToTensor()])

train_dataset = EEGDataset(root_path, metadata_train, composed)
val_dataset = EEGDataset(root_path, metadata_val, composed)
test_dataset = EEGDataset(root_path, metadata_test, composed)

print(train_dataset[0]['signal'].shape)
print(train_dataset[0])

print()
print('-' * 100)
print()

print(val_dataset[0]['signal'].shape)
print(val_dataset[0])

print()
print('-' * 100)
print()

print(test_dataset[0]['signal'].shape)
print(test_dataset[0])

torch.Size([20, 3000])
{'signal': tensor([[-0.7024, -0.7817, -0.6232,  ..., -0.9401, -1.0194, -1.1779],
        [-0.3727, -0.3727, -0.3727,  ..., -1.8077, -1.9180, -2.0284],
        [ 0.4031,  0.4031,  0.4031,  ..., -1.0705, -0.7758, -0.3337],
        ...,
        [ 0.1855,  0.2618,  0.2618,  ...,  1.1783,  1.3310,  1.4837],
        [ 0.5590,  0.5590,  0.3459,  ...,  0.7720,  0.9850,  1.1980],
        [-0.2778, -0.2254, -0.1467,  ...,  0.0107,  0.0632,  0.1025]]), 'age': tensor(-0.1827), 'class_label': tensor(2), 'metadata': {'serial': '15898', 'edfname': '00906868_040116', 'birth': '1946-04-09', 'record': '2016-01-04T11:46:11', 'age': 69, 'dx1': 'load', 'label': ['dementia', 'ad', 'load'], 'events': [[1218, 'Photic Off'], [2352, 'sleep']], 'class_type': 'Non-vascular dementia', 'class_label': 2}}

----------------------------------------------------------------------------------------------------

torch.Size([20, 3000])
{'signal': tensor([[ 0.5100,  0.4594,  0.3750,  ..., -0.3843, -0.4180, -0.2662],
        [-0.8759, -0.4898, -0.6001,  ...,  0.0618,  0.3376,  0.0066],
        [-4.2338, -4.1658, -4.0524,  ..., -0.5141, -0.4914, -0.4461],
        ...,
        [ 0.5780,  0.6232,  0.6684,  ...,  0.5780,  0.5780,  0.6006],
        [ 4.3516,  4.3334,  4.3151,  ..., -0.0905, -0.0539, -0.0539],
        [ 0.6250,  0.8325,  0.9017,  ..., -1.1561, -1.1734, -0.9486]]), 'age': tensor(0.3425), 'class_label': tensor(0), 'metadata': {'serial': '13037', 'edfname': '00803920_221217', 'birth': '1943-11-11', 'record': '2017-12-22T14:07:22', 'age': 74, 'dx1': 'smi', 'label': ['normal', 'smi'], 'events': [[1718, 'Eyes Open']], 'class_type': 'Normal', 'class_label': 0}}

----------------------------------------------------------------------------------------------------

torch.Size([20, 3000])
{'signal': tensor([[ 1.6333,  1.5879,  1.6787,  ..., -1.0468, -1.1377, -0.8651],
        [-1.6127, -1.6127, -0.8902,  ..., -0.7698, -0.7698, -0.4086],
        [-1.2926, -1.8401, -0.7451,  ...,  2.2662,  2.4031,  2.2662],
        ...,
        [-1.5804, -1.8490, -1.5804,  ...,  0.8362,  0.7019,  0.4334],
        [ 0.7683,  0.9682,  0.9682,  ...,  1.3681,  1.1682,  1.1682],
        [-0.0542, -0.0689, -0.1275,  ...,  0.4737,  0.6497,  0.7524]]), 'age': tensor(0.2374), 'class_label': tensor(1), 'metadata': {'serial': '14546', 'edfname': '00865039_170816', 'birth': '1943-07-02', 'record': '2016-08-17T09:46:02', 'age': 73, 'dx1': 'mci amnestic', 'label': ['mci', 'mci_amnestic'], 'events': [[482, 'Photic Off'], [2498, 'Photic On - 12.0 Hz'], [4514, 'Photic Off']], 'class_type': 'Non-vascular MCI', 'class_label': 1}}

Data loader test

print('Current PyTorch device:', device)
if device.type == 'cuda':
    num_workers = 0 # A number other than 0 causes an error
    pin_memory = True
else:
    num_workers = 0
    pin_memory = False

train_loader = DataLoader(train_dataset, 
                          batch_size=32, 
                          shuffle=True, 
                          drop_last=True,
                          num_workers=num_workers, 
                          pin_memory=pin_memory,
                          collate_fn=eeg_collate_fn)

for i_batch, sample_batched in enumerate(train_loader):
    sample_batched['signal'].to(device)
    sample_batched['age'].to(device)
    sample_batched['class_label'].to(device)
    
    print(i_batch, 
          sample_batched['signal'].shape, 
          sample_batched['age'].shape, 
          sample_batched['class_label'].shape, 
          len(sample_batched['metadata']))
    
    if i_batch > 3:
        break

Current PyTorch device: cuda
0 torch.Size([32, 20, 3000]) torch.Size([32]) torch.Size([32]) 32
1 torch.Size([32, 20, 3000]) torch.Size([32]) torch.Size([32]) 32
2 torch.Size([32, 20, 3000]) torch.Size([32]) torch.Size([32]) 32
3 torch.Size([32, 20, 3000]) torch.Size([32]) torch.Size([32]) 32
4 torch.Size([32, 20, 3000]) torch.Size([32]) torch.Size([32]) 32

Train, validation, test dataloaders

train_loader = DataLoader(train_dataset, 
                          batch_size=32, 
                          shuffle=True, 
                          drop_last=True,
                          num_workers=num_workers, 
                          pin_memory=pin_memory,
                          collate_fn=eeg_collate_fn)

val_loader = DataLoader(val_dataset, 
                        batch_size=32, 
                        shuffle=False, 
                        drop_last=False,
                        num_workers=num_workers, 
                        pin_memory=pin_memory,
                        collate_fn=eeg_collate_fn)

test_loader = DataLoader(test_dataset, 
                         batch_size=32, 
                         shuffle=False, 
                         drop_last=False,
                         num_workers=num_workers, 
                         pin_memory=pin_memory,
                         collate_fn=eeg_collate_fn)

---

Some useful functions for training

def count_parameters(model):
    return sum(p.numel() for p in model.parameters() if p.requires_grad)


def visualize_network_tensorboard(model, name):
    from torch.utils.tensorboard import SummaryWriter
    import ipynbname
    nb_fname = ipynbname.name()

    # default `log_dir` is "runs" - we'll be more specific here
    writer = SummaryWriter('runs/' + nb_fname + '_' + name)

    for batch_i, sample_batched in enumerate(train_loader):
        # pull up the batch data
        x = sample_batched['signal'].to(device)
        age = sample_batched['age'].to(device)
        target = sample_batched['class_label'].to(device)

        # apply model on whole batch directly on device
        writer.add_graph(model, (x, age))
        # output = model(x, age)
        break
        
    writer.close()

def train_one_epoch(model, optimizer, log_interval):
    # turn the models to training mode
    model.train()
    losses = []
    correct, total = (0, 0)
    
    C = len(class_label_to_type)
    train_confusion = np.zeros((C, C), dtype=np.int32)
    
    for batch_i, sample_batched in enumerate(train_loader):
        # pull up the batch data
        x = sample_batched['signal'].to(device)
        age = sample_batched['age'].to(device)
        target = sample_batched['class_label'].to(device)
        
        # apply model on whole batch directly on device
        output = model(x, age)

        # negative log-likelihood for a tensor of size (batch x n_output)
        pred = F.log_softmax(output, dim=1)
        loss = F.nll_loss(pred, target)
        
        # backprop and update
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()
        
        # record loss
        losses.append(loss.item())

        # train accuracy
        pred = pred.argmax(dim=-1)
        correct += pred.squeeze().eq(target).sum().item()
        total += pred.shape[0]
        
        # confusion matrix
        train_confusion += calculate_confusion_matrix(pred, target)

        # print training stats
        if log_interval is not None and (batch_i + 1) % log_interval == 0:
            print(f'- Iter {batch_i + 1:03d} / {len(train_loader):03d}, Loss: {loss.item():.06f}')
    
    train_accuracy = 100.0 * correct / total
    return (losses, train_accuracy, train_confusion)


def check_val_accuracy(model, repeat=1):
    model.eval()
    correct, total = (0, 0)
        
    C = len(class_label_to_type)
    val_confusion = np.zeros((C, C), dtype=np.int32)
    
    for k in range(repeat):
        for sample_batched in val_loader:
            # pull up the data
            x = sample_batched['signal'].to(device)
            age = sample_batched['age'].to(device)
            target = sample_batched['class_label'].to(device)

            # apply model on whole batch directly on device
            output = model(x, age)
            pred = F.log_softmax(output, dim=1)

            # val accuracy
            pred = pred.argmax(dim=-1)
            correct += pred.squeeze().eq(target).sum().item()
            total += pred.shape[0]
            
            # confusion matrix
            val_confusion += calculate_confusion_matrix(pred, target)
            
    val_accuracy = 100.0 * correct / total
    return (val_accuracy, val_confusion)

    
def check_test_accuracy(model, repeat=1):
    model.eval()
    correct, total = (0, 0)
    
    C = len(class_label_to_type)
    test_confusion = np.zeros((C, C), dtype=np.int32)
    
    test_debug = {data['metadata']['serial']: 
                  {'GT': data['class_label'].item(), 
                   'Acc': 0, 
                   'Pred': [0] * C} for data in test_dataset}
        
    for k in range(repeat):
        for sample_batched in test_loader:
            # pull up the data
            x = sample_batched['signal'].to(device)
            age = sample_batched['age'].to(device)
            target = sample_batched['class_label'].to(device)

            # apply model on whole batch directly on device
            output = model(x, age)
            pred = F.log_softmax(output, dim=1)
            
            # test accuracy
            pred = pred.argmax(dim=-1)
            correct += pred.squeeze().eq(target).sum().item()
            total += pred.shape[0]
            
            # confusion matrix
            test_confusion += calculate_confusion_matrix(pred, target)
            
            # test debug
            for n in range(pred.shape[0]):
                serial = sample_batched['metadata'][n]['serial']
                test_debug[serial]['edfname'] = sample_batched['metadata'][n]['edfname']
                test_debug[serial]['Pred'][pred[n].item()] += 1
                acc = test_debug[serial]['Pred'][target[n].item()] / np.sum(test_debug[serial]['Pred']) * 100
                test_debug[serial]['Acc'] = f'{acc:>6.02f}%'
        
    test_accuracy = 100.0 * correct / total
    return (test_accuracy, test_confusion, test_debug)


def calculate_confusion_matrix(pred, target):
    N = target.shape[0]
    C = len(class_label_to_type)
    confusion = np.zeros((C, C), dtype=np.int32)
    
    for i in range(N):
        r = target[i]
        c = pred[i]
        confusion[r, c] += 1
    return confusion

def draw_loss_plot(loss_history):
    plt.style.use('default') # default, ggplot, fivethirtyeight, classic
    
    fig = plt.figure(num=1, clear=True, figsize=(15.0, 6.0), constrained_layout=True)
    ax = fig.add_subplot(1, 1, 1)
    ax.plot(loss_history)
    
    ax.vlines(0, 0, 1, transform=ax.get_xaxis_transform(), colors='k', alpha=0.1)    
    for e in range(1, n_epoch + 1):
        
        if e % lr_schedule_step == 0:
            ax.vlines(e*len(train_loader) - 1, 0, 1, transform=ax.get_xaxis_transform(), colors='m', alpha=0.3)
        else:
            ax.vlines(e*len(train_loader) - 1, 0, 1, transform=ax.get_xaxis_transform(), colors='k', alpha=0.1)
    
    ax.set_title('Loss Plot')
    ax.set_xlabel('Iteration')
    ax.set_ylabel('Training Loss')
    
    plt.show()
    fig.clear()
    plt.close(fig)


def draw_accuracy_history(train_acc_history, val_acc_history):
    plt.style.use('default') # default, ggplot, fivethirtyeight, classic
    
    fig = plt.figure(num=1, clear=True, figsize=(15.0, 6.0), constrained_layout=True)
    ax = fig.add_subplot(1, 1, 1)
    ax.plot(train_acc_history, 'r-', label='Train accuracy')
    ax.plot(val_acc_history, 'b-', label='Validation accuracy')
    ax.legend(loc='lower right')
    
    ax.set_title('Accuracy Plot during Training')
    ax.set_xlabel('Epoch')
    ax.set_ylabel('Accuracy (%)')
    
    plt.show()
    fig.clear()
    plt.close(fig)


def draw_confusion(confusion):
    C = len(class_label_to_type)
    
    plt.style.use('default') # default, ggplot, fivethirtyeight, classic
    plt.rcParams['image.cmap'] = 'jet' # 'nipy_spectral'

    fig = plt.figure(num=1, clear=True, figsize=(5.0, 5.0), constrained_layout=True)
    ax = fig.add_subplot(1, 1, 1)
    im = ax.imshow(confusion, alpha=0.8)

    ax.set_xticks(np.arange(C))
    ax.set_yticks(np.arange(C))
    ax.set_xticklabels(class_label_to_type)
    ax.set_yticklabels(class_label_to_type)
    
    for r in range(C):
        for c in range(C):
            text = ax.text(c, r, confusion[r, c],
                           ha="center", va="center", color='k')
    
    ax.set_title('Confusion Matrix')
    ax.set_xlabel('Prediction')
    ax.set_ylabel('Ground Truth')
    plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor")
    
    plt.show()
    fig.clear()
    plt.close(fig)

def learning_rate_search(module, min_log_lr, max_log_lr, trials, epochs):
    learning_rate_record = []
    for t in tqdm(range(trials)):
        log_lr = np.random.uniform(min_log_lr, max_log_lr)
        lr = 10 ** log_lr
        
        module.reset_weights()
        optimizer = optim.AdamW(model.parameters(), lr=lr, weight_decay=0.0001)

        for e in range(epochs):
            _, train_accuracy, _ = train_one_epoch(model, optimizer, log_interval=None)
        
        # Train accuracy for the final epoch is stored
        learning_rate_record.append((log_lr, train_accuracy))
    
    return learning_rate_record


def draw_learning_rate_record(learning_rate_record):
    plt.style.use('default') # default, ggplot, fivethirtyeight, classic

    fig = plt.figure(num=1, clear=True, figsize=(8.0, 8.0), constrained_layout=True)
    ax = fig.add_subplot(1, 1, 1)
    
    ax.set_title('Learning Rate Search')
    ax.set_xlabel('Learning rate in log-scale')
    ax.set_ylabel('Train accuracy')
    
    for log_lr, val_accuracy in learning_rate_record:
        ax.scatter(log_lr, val_accuracy, c='r',
                   alpha=0.5, edgecolors='none')
    
    plt.show()
    fig.clear()
    plt.close(fig)

---

Train 1D CNN models

1D CNN: Tiny model

class TinyNet(nn.Module):
    def __init__(self, n_input=20, n_output=3, stride=7, n_channel=64, 
                 use_age=True, final_pool='average'):
        super().__init__()
        
        if final_pool not in {'average', 'max'}:
            raise ValueError("final_pool must be set to one of ['average', 'max']")
        
        self.use_age = use_age
        
        self.conv1 = nn.Conv1d(n_input, n_channel, kernel_size=35, stride=stride)
        self.bn1 = nn.BatchNorm1d(n_channel)
        self.pool1 = nn.MaxPool1d(5)
        
        self.conv2 = nn.Conv1d(n_channel, n_channel, kernel_size=7)
        self.bn2 = nn.BatchNorm1d(n_channel)
        self.pool2 = nn.MaxPool1d(2)
        
        if final_pool == 'average':
            self.final_pool = nn.AdaptiveAvgPool1d(1)
        elif final_pool == 'max':
            self.final_pool = nn.AdaptiveMaxPool1d(1)
        
        if self.use_age:        
            self.fc1 = nn.Linear(n_channel + 1, n_channel)
        else:
            self.fc1 = nn.Linear(n_channel, n_channel)
            
        self.dropout = nn.Dropout(p=0.3)
        self.bnfc1 = nn.BatchNorm1d(n_channel)
        self.fc2 = nn.Linear(n_channel, n_output)
        
    def reset_weights(self):
        for m in self.modules():
            if hasattr(m, 'reset_parameters'):
                m.reset_parameters()

    def forward(self, x, age):
        # conv-bn-relu-pool 
        x = self.conv1(x)
        x = F.relu(self.bn1(x))
        x = self.pool1(x)

        x = self.conv2(x)
        x = F.relu(self.bn2(x))
        x = self.pool2(x)
        
        x = self.final_pool(x).squeeze()
        if self.use_age:
            x = torch.cat((x, age.reshape(-1, 1)), dim=1)

        # fc-bn-dropout-relu-fc
        x = self.fc1(x)
        x = self.bnfc1(x)
        x = self.dropout(x)
        x = F.relu(x)
        x = self.fc2(x)
        
        return x
        # return F.log_softmax(x, dim=1)

model = TinyNet(n_input=train_dataset[0]['signal'].shape[0], 
                n_output=3, 
                use_age=True, 
                final_pool='max')

model = model.to(device, dtype=torch.float32)
print(model)
print()

# tensorboard visualization
visualize_network_tensorboard(model, 'TinyNet')

# number of parameters
n = count_parameters(model)
print(f'The Number of parameters of the model: {n:,}')

TinyNet(
  (conv1): Conv1d(20, 64, kernel_size=(35,), stride=(7,))
  (bn1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (pool1): MaxPool1d(kernel_size=5, stride=5, padding=0, dilation=1, ceil_mode=False)
  (conv2): Conv1d(64, 64, kernel_size=(7,), stride=(1,))
  (bn2): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (pool2): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (final_pool): AdaptiveMaxPool1d(output_size=1)
  (fc1): Linear(in_features=65, out_features=64, bias=True)
  (dropout): Dropout(p=0.3, inplace=False)
  (bnfc1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (fc2): Linear(in_features=64, out_features=3, bias=True)
)

C:\Users\IPIS-Minjae\anaconda3\envs\EEG_Project\lib\site-packages\torch\nn\functional.py:652: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  ..\c10/core/TensorImpl.h:1156.)
  return torch.max_pool1d(input, kernel_size, stride, padding, dilation, ceil_mode)

The Number of parameters of the model: 78,403

# record = learning_rate_search(model, 
#                               min_log_lr=-4.5, 
#                               max_log_lr=-1.0, 
#                               trials=500, 
#                               epochs=1)
# draw_learning_rate_record(record)
# best_log_lr = record[np.argmax(np.array([v for lr, v in record]))][0]

best_log_lr = -1.9358023684588126
print('best_log_lr:', best_log_lr)

best_log_lr: -1.9358023684588126

# reduce the learning after [lr_schedule_step] epochs by a factor of 10
n_epoch = 200
lr_schedule_step = 80
log_interval = len(train_loader) // 5

loss_history = []
train_acc_history = []
val_acc_history = []

model.reset_weights()
optimizer = optim.AdamW(model.parameters(), lr=10 ** best_log_lr, weight_decay=0.0001)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=lr_schedule_step, gamma=0.1)

print(f'{"*"*40} Training Starts {"*"*40}')

for epoch in tqdm(range(1, n_epoch + 1)):
    print(f'{"-"*30} Epoch {epoch:03d} / {n_epoch:03d}, Learning rate: {optimizer.param_groups[-1]["lr"]:.2e} {"-"*30}')
    
    # train
    loss, train_accuracy, train_confusion = train_one_epoch(model, optimizer, log_interval)
    loss_history.extend(loss)
    train_acc_history.append(train_accuracy)
    
    # validation
    val_accuracy, val_confusion = check_val_accuracy(model)
    val_acc_history.append(val_accuracy)
    
    # learning rate schedule
    scheduler.step()
    
    print()
    print(f'* Train accuracy / confusion: {train_accuracy:.2f}% / {train_confusion.tolist()}, ')
    print(f'* Val   accuracy / confusion: {val_accuracy:.2f}% / {val_confusion.tolist()}')
    print()
    
# test
test_accuracy, test_confusion, test_debug = check_test_accuracy(model, repeat=30)

print(f'{"*"*40} Training Ends {"*"*40}')
print(f'- Test accuracy: {test_accuracy:.2f}%')
print()
print('- Confusion matrix:\n', test_confusion)
print()

# draw the training loss plot
draw_loss_plot(loss_history)
draw_accuracy_history(train_acc_history, val_acc_history)

# draw the confusion matrix
draw_confusion(test_confusion)

**************************************** Training Starts ****************************************

------------------------------ Epoch 001 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 1.004954
- Iter 262 / 659, Loss: 0.941724
- Iter 393 / 659, Loss: 0.984434
- Iter 524 / 659, Loss: 1.003098
- Iter 655 / 659, Loss: 0.868365

* Train accuracy / confusion: 51.13% / [[5735, 2319, 490], [2626, 3741, 1122], [1257, 2492, 1306]], 
* Val   accuracy / confusion: 55.30% / [[761, 247, 61], [289, 433, 216], [103, 264, 266]]

------------------------------ Epoch 002 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.807459
- Iter 262 / 659, Loss: 0.826642
- Iter 393 / 659, Loss: 0.792830
- Iter 524 / 659, Loss: 0.931876
- Iter 655 / 659, Loss: 0.781200

* Train accuracy / confusion: 54.25% / [[5707, 2430, 407], [2389, 3935, 1165], [806, 2451, 1798]], 
* Val   accuracy / confusion: 56.06% / [[736, 237, 96], [271, 359, 308], [81, 167, 385]]

------------------------------ Epoch 003 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.889535
- Iter 262 / 659, Loss: 0.764320
- Iter 393 / 659, Loss: 0.916737
- Iter 524 / 659, Loss: 0.832490
- Iter 655 / 659, Loss: 0.934328

* Train accuracy / confusion: 56.34% / [[5816, 2298, 426], [2406, 3948, 1138], [698, 2240, 2118]], 
* Val   accuracy / confusion: 56.29% / [[773, 281, 15], [297, 557, 84], [107, 370, 156]]

------------------------------ Epoch 004 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.856948
- Iter 262 / 659, Loss: 1.059693
- Iter 393 / 659, Loss: 0.743412
- Iter 524 / 659, Loss: 0.838704
- Iter 655 / 659, Loss: 0.903201

* Train accuracy / confusion: 57.32% / [[5838, 2308, 395], [2270, 3978, 1244], [567, 2217, 2271]], 
* Val   accuracy / confusion: 56.44% / [[897, 152, 20], [426, 432, 80], [182, 290, 161]]

------------------------------ Epoch 005 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 1.002338
- Iter 262 / 659, Loss: 1.037957
- Iter 393 / 659, Loss: 0.746120
- Iter 524 / 659, Loss: 0.791034
- Iter 655 / 659, Loss: 0.633828

* Train accuracy / confusion: 58.13% / [[5883, 2268, 391], [2272, 3999, 1217], [595, 2086, 2377]], 
* Val   accuracy / confusion: 59.51% / [[800, 242, 27], [296, 504, 138], [89, 277, 267]]

------------------------------ Epoch 006 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.781253
- Iter 262 / 659, Loss: 0.771594
- Iter 393 / 659, Loss: 0.807154
- Iter 524 / 659, Loss: 0.799982
- Iter 655 / 659, Loss: 0.746459

* Train accuracy / confusion: 59.03% / [[5892, 2270, 378], [2153, 4069, 1270], [510, 2058, 2488]], 
* Val   accuracy / confusion: 61.02% / [[793, 260, 16], [266, 568, 104], [84, 299, 250]]

------------------------------ Epoch 007 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.861284
- Iter 262 / 659, Loss: 0.734630
- Iter 393 / 659, Loss: 0.938990
- Iter 524 / 659, Loss: 1.040277
- Iter 655 / 659, Loss: 0.667364

* Train accuracy / confusion: 60.48% / [[6061, 2131, 354], [2162, 4142, 1182], [510, 1996, 2550]], 
* Val   accuracy / confusion: 63.03% / [[751, 279, 39], [210, 557, 171], [47, 230, 356]]

------------------------------ Epoch 008 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.936021
- Iter 262 / 659, Loss: 0.711429
- Iter 393 / 659, Loss: 0.741653
- Iter 524 / 659, Loss: 0.751267
- Iter 655 / 659, Loss: 0.909676

* Train accuracy / confusion: 61.74% / [[6044, 2126, 368], [2094, 4266, 1136], [454, 1891, 2709]], 
* Val   accuracy / confusion: 62.92% / [[677, 352, 40], [148, 620, 170], [32, 237, 364]]

------------------------------ Epoch 009 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.691435
- Iter 262 / 659, Loss: 0.817152
- Iter 393 / 659, Loss: 0.721956
- Iter 524 / 659, Loss: 0.945982
- Iter 655 / 659, Loss: 0.650148

* Train accuracy / confusion: 62.40% / [[6068, 2072, 403], [2090, 4318, 1081], [477, 1806, 2773]], 
* Val   accuracy / confusion: 64.51% / [[830, 199, 40], [261, 520, 157], [73, 207, 353]]

------------------------------ Epoch 010 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.860439
- Iter 262 / 659, Loss: 0.845404
- Iter 393 / 659, Loss: 0.765296
- Iter 524 / 659, Loss: 0.908934
- Iter 655 / 659, Loss: 0.846960

* Train accuracy / confusion: 63.15% / [[6094, 2060, 388], [2087, 4349, 1059], [437, 1739, 2875]], 
* Val   accuracy / confusion: 63.94% / [[842, 207, 20], [299, 535, 104], [104, 218, 311]]

------------------------------ Epoch 011 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.850586
- Iter 262 / 659, Loss: 0.716395
- Iter 393 / 659, Loss: 0.634740
- Iter 524 / 659, Loss: 1.012816
- Iter 655 / 659, Loss: 0.717152

* Train accuracy / confusion: 64.15% / [[6115, 2074, 359], [1997, 4445, 1042], [438, 1650, 2968]], 
* Val   accuracy / confusion: 63.90% / [[620, 367, 82], [115, 618, 205], [18, 166, 449]]

------------------------------ Epoch 012 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.720060
- Iter 262 / 659, Loss: 0.699611
- Iter 393 / 659, Loss: 0.620865
- Iter 524 / 659, Loss: 0.783245
- Iter 655 / 659, Loss: 0.969087

* Train accuracy / confusion: 64.95% / [[6175, 2016, 351], [1978, 4511, 1003], [411, 1632, 3011]], 
* Val   accuracy / confusion: 65.49% / [[761, 287, 21], [195, 673, 70], [54, 284, 295]]

------------------------------ Epoch 013 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.721198
- Iter 262 / 659, Loss: 0.883859
- Iter 393 / 659, Loss: 0.747758
- Iter 524 / 659, Loss: 0.668222
- Iter 655 / 659, Loss: 1.144060

* Train accuracy / confusion: 65.30% / [[6178, 2017, 347], [1982, 4513, 994], [412, 1565, 3080]], 
* Val   accuracy / confusion: 62.27% / [[970, 77, 22], [492, 336, 110], [165, 130, 338]]

------------------------------ Epoch 014 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.679020
- Iter 262 / 659, Loss: 0.743413
- Iter 393 / 659, Loss: 0.787290
- Iter 524 / 659, Loss: 0.703701
- Iter 655 / 659, Loss: 0.729938

* Train accuracy / confusion: 66.73% / [[6285, 1940, 321], [1912, 4631, 946], [381, 1515, 3157]], 
* Val   accuracy / confusion: 65.61% / [[743, 296, 30], [197, 672, 69], [58, 258, 317]]

------------------------------ Epoch 015 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.823629
- Iter 262 / 659, Loss: 0.754329
- Iter 393 / 659, Loss: 0.815744
- Iter 524 / 659, Loss: 0.795734
- Iter 655 / 659, Loss: 0.615430

* Train accuracy / confusion: 66.57% / [[6260, 1925, 354], [1937, 4609, 953], [384, 1496, 3170]], 
* Val   accuracy / confusion: 66.21% / [[871, 140, 58], [308, 427, 203], [72, 111, 450]]

------------------------------ Epoch 016 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.710018
- Iter 262 / 659, Loss: 0.875025
- Iter 393 / 659, Loss: 0.649422
- Iter 524 / 659, Loss: 0.730108
- Iter 655 / 659, Loss: 0.807564

* Train accuracy / confusion: 67.81% / [[6303, 1909, 329], [1846, 4718, 925], [369, 1411, 3278]], 
* Val   accuracy / confusion: 67.92% / [[835, 211, 23], [261, 614, 63], [59, 230, 344]]

------------------------------ Epoch 017 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.668633
- Iter 262 / 659, Loss: 0.647011
- Iter 393 / 659, Loss: 0.559856
- Iter 524 / 659, Loss: 0.661541
- Iter 655 / 659, Loss: 0.495009

* Train accuracy / confusion: 68.01% / [[6271, 1933, 342], [1806, 4775, 905], [343, 1416, 3297]], 
* Val   accuracy / confusion: 66.40% / [[774, 245, 50], [234, 542, 162], [47, 149, 437]]

------------------------------ Epoch 018 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.614100
- Iter 262 / 659, Loss: 0.771228
- Iter 393 / 659, Loss: 0.784902
- Iter 524 / 659, Loss: 0.651293
- Iter 655 / 659, Loss: 0.654913

* Train accuracy / confusion: 68.17% / [[6403, 1821, 323], [1884, 4660, 941], [376, 1368, 3312]], 
* Val   accuracy / confusion: 68.86% / [[805, 246, 18], [214, 665, 59], [59, 226, 348]]

------------------------------ Epoch 019 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.578572
- Iter 262 / 659, Loss: 0.755090
- Iter 393 / 659, Loss: 0.799642
- Iter 524 / 659, Loss: 0.987494
- Iter 655 / 659, Loss: 0.811614

* Train accuracy / confusion: 68.66% / [[6361, 1849, 333], [1822, 4805, 861], [368, 1377, 3312]], 
* Val   accuracy / confusion: 67.92% / [[861, 186, 22], [284, 562, 92], [76, 187, 370]]

------------------------------ Epoch 020 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.675774
- Iter 262 / 659, Loss: 0.610624
- Iter 393 / 659, Loss: 0.712255
- Iter 524 / 659, Loss: 0.732015
- Iter 655 / 659, Loss: 0.832041

* Train accuracy / confusion: 68.96% / [[6355, 1856, 331], [1791, 4875, 823], [370, 1374, 3313]], 
* Val   accuracy / confusion: 66.14% / [[943, 104, 22], [406, 429, 103], [129, 130, 374]]

------------------------------ Epoch 021 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.565357
- Iter 262 / 659, Loss: 0.699870
- Iter 393 / 659, Loss: 0.906394
- Iter 524 / 659, Loss: 0.513849
- Iter 655 / 659, Loss: 0.927456

* Train accuracy / confusion: 70.26% / [[6484, 1739, 315], [1769, 4902, 822], [347, 1279, 3431]], 
* Val   accuracy / confusion: 69.28% / [[764, 238, 67], [172, 567, 199], [37, 98, 498]]

------------------------------ Epoch 022 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.689649
- Iter 262 / 659, Loss: 0.673445
- Iter 393 / 659, Loss: 0.667479
- Iter 524 / 659, Loss: 0.657666
- Iter 655 / 659, Loss: 0.634674

* Train accuracy / confusion: 70.36% / [[6464, 1751, 326], [1762, 4889, 843], [358, 1210, 3485]], 
* Val   accuracy / confusion: 69.70% / [[852, 197, 20], [239, 623, 76], [73, 195, 365]]

------------------------------ Epoch 023 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.766160
- Iter 262 / 659, Loss: 0.771516
- Iter 393 / 659, Loss: 0.572340
- Iter 524 / 659, Loss: 0.520884
- Iter 655 / 659, Loss: 0.864722

* Train accuracy / confusion: 71.01% / [[6549, 1671, 320], [1742, 4898, 851], [336, 1194, 3527]], 
* Val   accuracy / confusion: 66.10% / [[608, 364, 97], [104, 650, 184], [17, 129, 487]]

------------------------------ Epoch 024 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.599910
- Iter 262 / 659, Loss: 0.819052
- Iter 393 / 659, Loss: 0.597944
- Iter 524 / 659, Loss: 0.729442
- Iter 655 / 659, Loss: 0.788421

* Train accuracy / confusion: 70.96% / [[6582, 1651, 307], [1736, 4893, 866], [334, 1230, 3489]], 
* Val   accuracy / confusion: 69.92% / [[914, 132, 23], [319, 549, 70], [99, 151, 383]]

------------------------------ Epoch 025 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.827851
- Iter 262 / 659, Loss: 0.606638
- Iter 393 / 659, Loss: 0.580650
- Iter 524 / 659, Loss: 0.868398
- Iter 655 / 659, Loss: 0.687312

* Train accuracy / confusion: 71.42% / [[6521, 1701, 321], [1710, 4961, 814], [328, 1153, 3579]], 
* Val   accuracy / confusion: 70.42% / [[910, 119, 40], [301, 496, 141], [84, 96, 453]]

------------------------------ Epoch 026 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.526604
- Iter 262 / 659, Loss: 0.532460
- Iter 393 / 659, Loss: 0.754488
- Iter 524 / 659, Loss: 0.589696
- Iter 655 / 659, Loss: 0.811318

* Train accuracy / confusion: 71.95% / [[6564, 1646, 332], [1690, 5011, 792], [309, 1147, 3597]], 
* Val   accuracy / confusion: 70.53% / [[925, 117, 27], [320, 529, 89], [77, 148, 408]]

------------------------------ Epoch 027 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.622995
- Iter 262 / 659, Loss: 0.678451
- Iter 393 / 659, Loss: 0.682716
- Iter 524 / 659, Loss: 0.785640
- Iter 655 / 659, Loss: 0.561224

* Train accuracy / confusion: 71.85% / [[6603, 1599, 342], [1747, 4959, 786], [313, 1150, 3589]], 
* Val   accuracy / confusion: 70.23% / [[822, 195, 52], [228, 559, 151], [46, 114, 473]]

------------------------------ Epoch 028 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.744125
- Iter 262 / 659, Loss: 0.554050
- Iter 393 / 659, Loss: 0.594954
- Iter 524 / 659, Loss: 0.652798
- Iter 655 / 659, Loss: 0.708556

* Train accuracy / confusion: 72.50% / [[6583, 1633, 324], [1637, 5049, 806], [311, 1089, 3656]], 
* Val   accuracy / confusion: 70.64% / [[803, 249, 17], [187, 712, 39], [47, 236, 350]]

------------------------------ Epoch 029 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.708278
- Iter 262 / 659, Loss: 0.593127
- Iter 393 / 659, Loss: 1.115590
- Iter 524 / 659, Loss: 0.672008
- Iter 655 / 659, Loss: 0.562957

* Train accuracy / confusion: 72.76% / [[6625, 1613, 306], [1669, 5054, 768], [315, 1073, 3665]], 
* Val   accuracy / confusion: 67.92% / [[828, 124, 117], [208, 401, 329], [26, 43, 564]]

------------------------------ Epoch 030 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.850628
- Iter 262 / 659, Loss: 0.672559
- Iter 393 / 659, Loss: 0.600180
- Iter 524 / 659, Loss: 0.666817
- Iter 655 / 659, Loss: 0.562210

* Train accuracy / confusion: 72.70% / [[6631, 1602, 317], [1684, 5039, 765], [299, 1091, 3660]], 
* Val   accuracy / confusion: 70.11% / [[852, 209, 8], [215, 699, 24], [71, 262, 300]]

------------------------------ Epoch 031 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.553788
- Iter 262 / 659, Loss: 0.547006
- Iter 393 / 659, Loss: 0.540528
- Iter 524 / 659, Loss: 0.641751
- Iter 655 / 659, Loss: 0.610825

* Train accuracy / confusion: 73.12% / [[6592, 1625, 328], [1599, 5157, 733], [311, 1073, 3670]], 
* Val   accuracy / confusion: 62.54% / [[514, 548, 7], [60, 846, 32], [21, 321, 291]]

------------------------------ Epoch 032 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.472778
- Iter 262 / 659, Loss: 0.896372
- Iter 393 / 659, Loss: 0.562158
- Iter 524 / 659, Loss: 0.584833
- Iter 655 / 659, Loss: 0.363134

* Train accuracy / confusion: 73.26% / [[6629, 1593, 319], [1607, 5089, 798], [290, 1031, 3732]], 
* Val   accuracy / confusion: 71.63% / [[892, 148, 29], [266, 565, 107], [74, 125, 434]]

------------------------------ Epoch 033 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.931455
- Iter 262 / 659, Loss: 0.575038
- Iter 393 / 659, Loss: 0.647147
- Iter 524 / 659, Loss: 0.647081
- Iter 655 / 659, Loss: 0.503219

* Train accuracy / confusion: 74.08% / [[6688, 1558, 295], [1556, 5173, 762], [288, 1007, 3761]], 
* Val   accuracy / confusion: 70.64% / [[899, 158, 12], [258, 637, 43], [97, 207, 329]]

------------------------------ Epoch 034 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.557754
- Iter 262 / 659, Loss: 0.775677
- Iter 393 / 659, Loss: 0.746395
- Iter 524 / 659, Loss: 0.616130
- Iter 655 / 659, Loss: 0.625984

* Train accuracy / confusion: 74.01% / [[6671, 1555, 313], [1567, 5175, 750], [274, 1021, 3762]], 
* Val   accuracy / confusion: 70.19% / [[710, 302, 57], [136, 725, 77], [41, 174, 418]]

------------------------------ Epoch 035 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.472961
- Iter 262 / 659, Loss: 0.667715
- Iter 393 / 659, Loss: 0.493550
- Iter 524 / 659, Loss: 0.842779
- Iter 655 / 659, Loss: 0.625130

* Train accuracy / confusion: 74.26% / [[6762, 1494, 288], [1600, 5147, 738], [302, 1005, 3752]], 
* Val   accuracy / confusion: 72.95% / [[869, 173, 27], [214, 617, 107], [63, 130, 440]]

------------------------------ Epoch 036 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.669807
- Iter 262 / 659, Loss: 0.451508
- Iter 393 / 659, Loss: 0.591672
- Iter 524 / 659, Loss: 0.479247
- Iter 655 / 659, Loss: 0.804172

* Train accuracy / confusion: 74.54% / [[6693, 1533, 315], [1506, 5230, 755], [298, 961, 3797]], 
* Val   accuracy / confusion: 70.34% / [[684, 289, 96], [115, 658, 165], [22, 96, 515]]

------------------------------ Epoch 037 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.511587
- Iter 262 / 659, Loss: 0.552249
- Iter 393 / 659, Loss: 0.868528
- Iter 524 / 659, Loss: 0.787784
- Iter 655 / 659, Loss: 0.778812

* Train accuracy / confusion: 74.82% / [[6756, 1490, 298], [1537, 5255, 698], [315, 973, 3766]], 
* Val   accuracy / confusion: 71.78% / [[802, 195, 72], [187, 578, 173], [30, 88, 515]]

------------------------------ Epoch 038 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.652582
- Iter 262 / 659, Loss: 0.671301
- Iter 393 / 659, Loss: 0.596424
- Iter 524 / 659, Loss: 0.494332
- Iter 655 / 659, Loss: 0.632744

* Train accuracy / confusion: 75.29% / [[6766, 1462, 315], [1506, 5276, 708], [281, 939, 3835]], 
* Val   accuracy / confusion: 72.80% / [[858, 196, 15], [209, 651, 78], [55, 165, 413]]

------------------------------ Epoch 039 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.795666
- Iter 262 / 659, Loss: 0.497944
- Iter 393 / 659, Loss: 0.703604
- Iter 524 / 659, Loss: 0.790764
- Iter 655 / 659, Loss: 0.482994

* Train accuracy / confusion: 75.22% / [[6764, 1494, 285], [1503, 5266, 722], [286, 936, 3832]], 
* Val   accuracy / confusion: 69.43% / [[948, 106, 15], [371, 528, 39], [113, 163, 357]]

------------------------------ Epoch 040 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.423320
- Iter 262 / 659, Loss: 0.544542
- Iter 393 / 659, Loss: 0.618221
- Iter 524 / 659, Loss: 0.493836
- Iter 655 / 659, Loss: 0.554251

* Train accuracy / confusion: 75.38% / [[6785, 1477, 280], [1527, 5253, 711], [277, 919, 3859]], 
* Val   accuracy / confusion: 74.02% / [[833, 190, 46], [191, 634, 113], [50, 96, 487]]

------------------------------ Epoch 041 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.546362
- Iter 262 / 659, Loss: 0.426735
- Iter 393 / 659, Loss: 0.419347
- Iter 524 / 659, Loss: 0.555812
- Iter 655 / 659, Loss: 0.539130

* Train accuracy / confusion: 75.90% / [[6761, 1508, 274], [1410, 5393, 685], [311, 895, 3851]], 
* Val   accuracy / confusion: 73.90% / [[850, 187, 32], [191, 687, 60], [49, 170, 414]]

------------------------------ Epoch 042 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.830464
- Iter 262 / 659, Loss: 0.594886
- Iter 393 / 659, Loss: 0.387726
- Iter 524 / 659, Loss: 0.544705
- Iter 655 / 659, Loss: 0.590571

* Train accuracy / confusion: 75.56% / [[6826, 1430, 287], [1548, 5260, 681], [314, 894, 3848]], 
* Val   accuracy / confusion: 72.08% / [[856, 201, 12], [204, 694, 40], [78, 202, 353]]

------------------------------ Epoch 043 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.536554
- Iter 262 / 659, Loss: 0.670039
- Iter 393 / 659, Loss: 0.748263
- Iter 524 / 659, Loss: 0.480730
- Iter 655 / 659, Loss: 0.453874

* Train accuracy / confusion: 76.02% / [[6824, 1437, 281], [1450, 5362, 681], [295, 913, 3845]], 
* Val   accuracy / confusion: 73.41% / [[899, 148, 22], [236, 631, 71], [69, 156, 408]]

------------------------------ Epoch 044 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.840778
- Iter 262 / 659, Loss: 0.432360
- Iter 393 / 659, Loss: 0.435056
- Iter 524 / 659, Loss: 0.641159
- Iter 655 / 659, Loss: 0.718093

* Train accuracy / confusion: 76.78% / [[6871, 1379, 295], [1449, 5402, 641], [275, 857, 3919]], 
* Val   accuracy / confusion: 74.47% / [[900, 131, 38], [230, 572, 136], [56, 83, 494]]

------------------------------ Epoch 045 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.581251
- Iter 262 / 659, Loss: 0.740797
- Iter 393 / 659, Loss: 0.486245
- Iter 524 / 659, Loss: 0.548710
- Iter 655 / 659, Loss: 0.443030

* Train accuracy / confusion: 76.29% / [[6837, 1407, 295], [1445, 5339, 706], [286, 861, 3912]], 
* Val   accuracy / confusion: 72.23% / [[907, 98, 64], [254, 481, 203], [53, 61, 519]]

------------------------------ Epoch 046 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.713853
- Iter 262 / 659, Loss: 0.562266
- Iter 393 / 659, Loss: 0.886038
- Iter 524 / 659, Loss: 0.624221
- Iter 655 / 659, Loss: 0.592206

* Train accuracy / confusion: 76.33% / [[6871, 1411, 263], [1458, 5337, 697], [287, 875, 3889]], 
* Val   accuracy / confusion: 68.60% / [[910, 56, 103], [331, 334, 273], [42, 24, 567]]

------------------------------ Epoch 047 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.419824
- Iter 262 / 659, Loss: 0.518160
- Iter 393 / 659, Loss: 0.421815
- Iter 524 / 659, Loss: 0.518378
- Iter 655 / 659, Loss: 0.631657

* Train accuracy / confusion: 76.83% / [[6909, 1320, 317], [1438, 5367, 682], [272, 858, 3925]], 
* Val   accuracy / confusion: 74.02% / [[807, 231, 31], [154, 688, 96], [41, 133, 459]]

------------------------------ Epoch 048 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.566210
- Iter 262 / 659, Loss: 0.332559
- Iter 393 / 659, Loss: 0.755536
- Iter 524 / 659, Loss: 0.594633
- Iter 655 / 659, Loss: 0.672644

* Train accuracy / confusion: 76.66% / [[6839, 1423, 276], [1460, 5382, 649], [269, 844, 3946]], 
* Val   accuracy / confusion: 74.39% / [[832, 196, 41], [173, 672, 93], [56, 117, 460]]

------------------------------ Epoch 049 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.614862
- Iter 262 / 659, Loss: 0.677455
- Iter 393 / 659, Loss: 0.637939
- Iter 524 / 659, Loss: 0.498411
- Iter 655 / 659, Loss: 0.645335

* Train accuracy / confusion: 77.27% / [[6924, 1314, 307], [1447, 5401, 641], [275, 810, 3969]], 
* Val   accuracy / confusion: 75.15% / [[919, 111, 39], [260, 556, 122], [55, 69, 509]]

------------------------------ Epoch 050 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.789486
- Iter 262 / 659, Loss: 0.604971
- Iter 393 / 659, Loss: 0.421386
- Iter 524 / 659, Loss: 0.483975
- Iter 655 / 659, Loss: 0.573080

* Train accuracy / confusion: 77.10% / [[6924, 1347, 272], [1433, 5380, 677], [271, 830, 3954]], 
* Val   accuracy / confusion: 73.30% / [[759, 234, 76], [139, 696, 103], [28, 125, 480]]

------------------------------ Epoch 051 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.446673
- Iter 262 / 659, Loss: 0.381624
- Iter 393 / 659, Loss: 0.519387
- Iter 524 / 659, Loss: 0.721432
- Iter 655 / 659, Loss: 0.432862

* Train accuracy / confusion: 77.25% / [[6899, 1354, 288], [1421, 5443, 625], [282, 828, 3948]], 
* Val   accuracy / confusion: 71.63% / [[973, 88, 8], [340, 568, 30], [119, 164, 350]]

------------------------------ Epoch 052 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.315920
- Iter 262 / 659, Loss: 0.612544
- Iter 393 / 659, Loss: 0.569895
- Iter 524 / 659, Loss: 0.576442
- Iter 655 / 659, Loss: 0.494155

* Train accuracy / confusion: 76.83% / [[6861, 1387, 294], [1466, 5385, 642], [282, 816, 3955]], 
* Val   accuracy / confusion: 75.04% / [[861, 170, 38], [188, 633, 117], [40, 106, 487]]

------------------------------ Epoch 053 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.496823
- Iter 262 / 659, Loss: 0.567842
- Iter 393 / 659, Loss: 0.325979
- Iter 524 / 659, Loss: 0.460405
- Iter 655 / 659, Loss: 0.656841

* Train accuracy / confusion: 77.82% / [[6974, 1282, 285], [1383, 5469, 641], [282, 805, 3967]], 
* Val   accuracy / confusion: 73.26% / [[815, 226, 28], [178, 701, 59], [49, 166, 418]]

------------------------------ Epoch 054 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.628032
- Iter 262 / 659, Loss: 0.365045
- Iter 393 / 659, Loss: 0.512429
- Iter 524 / 659, Loss: 0.405985
- Iter 655 / 659, Loss: 0.537664

* Train accuracy / confusion: 78.11% / [[6965, 1309, 270], [1349, 5510, 630], [246, 813, 3996]], 
* Val   accuracy / confusion: 74.66% / [[845, 184, 40], [180, 675, 83], [59, 123, 451]]

------------------------------ Epoch 055 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.398933
- Iter 262 / 659, Loss: 0.560829
- Iter 393 / 659, Loss: 0.353087
- Iter 524 / 659, Loss: 0.463034
- Iter 655 / 659, Loss: 0.583575

* Train accuracy / confusion: 78.18% / [[6952, 1297, 296], [1329, 5556, 606], [273, 801, 3978]], 
* Val   accuracy / confusion: 73.48% / [[742, 258, 69], [106, 675, 157], [20, 90, 523]]

------------------------------ Epoch 056 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.343624
- Iter 262 / 659, Loss: 0.630366
- Iter 393 / 659, Loss: 0.420341
- Iter 524 / 659, Loss: 0.489720
- Iter 655 / 659, Loss: 0.506026

* Train accuracy / confusion: 78.45% / [[6981, 1305, 257], [1324, 5538, 629], [280, 749, 4025]], 
* Val   accuracy / confusion: 74.47% / [[823, 226, 20], [163, 690, 85], [50, 130, 453]]

------------------------------ Epoch 057 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.342452
- Iter 262 / 659, Loss: 0.690483
- Iter 393 / 659, Loss: 0.315605
- Iter 524 / 659, Loss: 0.345299
- Iter 655 / 659, Loss: 0.744031

* Train accuracy / confusion: 77.78% / [[6894, 1355, 295], [1367, 5492, 633], [276, 760, 4016]], 
* Val   accuracy / confusion: 74.36% / [[787, 261, 21], [129, 765, 44], [42, 180, 411]]

------------------------------ Epoch 058 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.251470
- Iter 262 / 659, Loss: 0.572336
- Iter 393 / 659, Loss: 0.330534
- Iter 524 / 659, Loss: 0.335168
- Iter 655 / 659, Loss: 0.622069

* Train accuracy / confusion: 78.38% / [[6984, 1289, 267], [1318, 5559, 613], [265, 808, 3985]], 
* Val   accuracy / confusion: 72.54% / [[756, 275, 38], [155, 708, 75], [43, 139, 451]]

------------------------------ Epoch 059 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.449057
- Iter 262 / 659, Loss: 0.559427
- Iter 393 / 659, Loss: 0.414914
- Iter 524 / 659, Loss: 0.710958
- Iter 655 / 659, Loss: 0.451670

* Train accuracy / confusion: 78.69% / [[7015, 1261, 268], [1373, 5539, 576], [286, 730, 4040]], 
* Val   accuracy / confusion: 73.60% / [[861, 195, 13], [171, 728, 39], [66, 213, 354]]

------------------------------ Epoch 060 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.520499
- Iter 262 / 659, Loss: 0.368385
- Iter 393 / 659, Loss: 0.555091
- Iter 524 / 659, Loss: 0.585617
- Iter 655 / 659, Loss: 0.476916

* Train accuracy / confusion: 78.48% / [[7001, 1260, 284], [1305, 5538, 646], [274, 769, 4011]], 
* Val   accuracy / confusion: 74.62% / [[798, 170, 101], [121, 636, 181], [19, 78, 536]]

------------------------------ Epoch 061 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.546112
- Iter 262 / 659, Loss: 0.522971
- Iter 393 / 659, Loss: 0.447349
- Iter 524 / 659, Loss: 0.447669
- Iter 655 / 659, Loss: 0.352472

* Train accuracy / confusion: 78.68% / [[7006, 1264, 275], [1342, 5509, 636], [261, 717, 4078]], 
* Val   accuracy / confusion: 75.04% / [[860, 192, 17], [179, 702, 57], [60, 154, 419]]

------------------------------ Epoch 062 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.522359
- Iter 262 / 659, Loss: 0.631503
- Iter 393 / 659, Loss: 0.898074
- Iter 524 / 659, Loss: 0.725644
- Iter 655 / 659, Loss: 0.384248

* Train accuracy / confusion: 78.91% / [[6997, 1276, 270], [1300, 5577, 618], [239, 744, 4067]], 
* Val   accuracy / confusion: 73.30% / [[786, 216, 67], [150, 679, 109], [21, 142, 470]]

------------------------------ Epoch 063 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.404390
- Iter 262 / 659, Loss: 0.501275
- Iter 393 / 659, Loss: 0.608641
- Iter 524 / 659, Loss: 0.395001
- Iter 655 / 659, Loss: 0.452203

* Train accuracy / confusion: 79.07% / [[6967, 1290, 290], [1268, 5659, 561], [244, 760, 4049]], 
* Val   accuracy / confusion: 74.36% / [[757, 265, 47], [115, 703, 120], [29, 101, 503]]

------------------------------ Epoch 064 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.319593
- Iter 262 / 659, Loss: 0.370092
- Iter 393 / 659, Loss: 0.597309
- Iter 524 / 659, Loss: 0.606549
- Iter 655 / 659, Loss: 0.411933

* Train accuracy / confusion: 79.41% / [[6999, 1238, 308], [1238, 5676, 574], [277, 707, 4071]], 
* Val   accuracy / confusion: 75.08% / [[885, 153, 31], [208, 666, 64], [55, 147, 431]]

------------------------------ Epoch 065 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.319319
- Iter 262 / 659, Loss: 0.611397
- Iter 393 / 659, Loss: 0.411500
- Iter 524 / 659, Loss: 0.346125
- Iter 655 / 659, Loss: 0.669557

* Train accuracy / confusion: 79.00% / [[7020, 1215, 310], [1304, 5593, 594], [266, 740, 4046]], 
* Val   accuracy / confusion: 72.84% / [[702, 286, 81], [94, 703, 141], [25, 90, 518]]

------------------------------ Epoch 066 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.595400
- Iter 262 / 659, Loss: 0.576845
- Iter 393 / 659, Loss: 0.542287
- Iter 524 / 659, Loss: 0.362777
- Iter 655 / 659, Loss: 0.577875

* Train accuracy / confusion: 79.24% / [[7000, 1258, 285], [1226, 5637, 629], [251, 729, 4073]], 
* Val   accuracy / confusion: 75.95% / [[891, 162, 16], [197, 700, 41], [60, 159, 414]]

------------------------------ Epoch 067 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.450142
- Iter 262 / 659, Loss: 0.444894
- Iter 393 / 659, Loss: 0.612792
- Iter 524 / 659, Loss: 0.788636
- Iter 655 / 659, Loss: 0.544424

* Train accuracy / confusion: 78.83% / [[7007, 1240, 297], [1275, 5598, 614], [278, 760, 4019]], 
* Val   accuracy / confusion: 75.95% / [[933, 114, 22], [237, 645, 56], [62, 144, 427]]

------------------------------ Epoch 068 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.428307
- Iter 262 / 659, Loss: 0.516731
- Iter 393 / 659, Loss: 0.483931
- Iter 524 / 659, Loss: 0.395034
- Iter 655 / 659, Loss: 0.510329

* Train accuracy / confusion: 79.41% / [[7060, 1219, 266], [1287, 5608, 592], [257, 721, 4078]], 
* Val   accuracy / confusion: 71.86% / [[640, 383, 46], [67, 816, 55], [22, 170, 441]]

------------------------------ Epoch 069 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.425048
- Iter 262 / 659, Loss: 0.709758
- Iter 393 / 659, Loss: 0.674233
- Iter 524 / 659, Loss: 0.581450
- Iter 655 / 659, Loss: 0.317446

* Train accuracy / confusion: 79.87% / [[7019, 1247, 276], [1195, 5721, 577], [239, 712, 4102]], 
* Val   accuracy / confusion: 76.70% / [[908, 119, 42], [211, 615, 112], [57, 74, 502]]

------------------------------ Epoch 070 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.460087
- Iter 262 / 659, Loss: 0.438093
- Iter 393 / 659, Loss: 0.689765
- Iter 524 / 659, Loss: 0.490802
- Iter 655 / 659, Loss: 0.377802

* Train accuracy / confusion: 79.88% / [[7108, 1194, 238], [1237, 5635, 617], [234, 723, 4102]], 
* Val   accuracy / confusion: 75.68% / [[899, 136, 34], [223, 619, 96], [63, 90, 480]]

------------------------------ Epoch 071 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.561455
- Iter 262 / 659, Loss: 1.001990
- Iter 393 / 659, Loss: 0.279086
- Iter 524 / 659, Loss: 0.487646
- Iter 655 / 659, Loss: 0.701302

* Train accuracy / confusion: 79.90% / [[7044, 1219, 281], [1242, 5673, 577], [250, 669, 4133]], 
* Val   accuracy / confusion: 71.36% / [[679, 374, 16], [80, 821, 37], [41, 208, 384]]

------------------------------ Epoch 072 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.269631
- Iter 262 / 659, Loss: 0.341856
- Iter 393 / 659, Loss: 0.493669
- Iter 524 / 659, Loss: 0.503464
- Iter 655 / 659, Loss: 0.455947

* Train accuracy / confusion: 80.26% / [[7115, 1147, 280], [1240, 5688, 565], [236, 695, 4122]], 
* Val   accuracy / confusion: 75.30% / [[878, 176, 15], [178, 721, 39], [66, 178, 389]]

------------------------------ Epoch 073 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.456220
- Iter 262 / 659, Loss: 0.275628
- Iter 393 / 659, Loss: 0.482628
- Iter 524 / 659, Loss: 0.581086
- Iter 655 / 659, Loss: 0.347557

* Train accuracy / confusion: 80.20% / [[7106, 1151, 284], [1205, 5703, 585], [249, 702, 4103]], 
* Val   accuracy / confusion: 75.68% / [[854, 149, 66], [169, 607, 162], [34, 62, 537]]

------------------------------ Epoch 074 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.502991
- Iter 262 / 659, Loss: 0.440590
- Iter 393 / 659, Loss: 0.456891
- Iter 524 / 659, Loss: 0.499029
- Iter 655 / 659, Loss: 0.470060

* Train accuracy / confusion: 80.32% / [[7084, 1185, 272], [1210, 5743, 541], [242, 700, 4111]], 
* Val   accuracy / confusion: 75.34% / [[919, 127, 23], [249, 629, 60], [75, 117, 441]]

------------------------------ Epoch 075 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.631037
- Iter 262 / 659, Loss: 0.755610
- Iter 393 / 659, Loss: 0.392537
- Iter 524 / 659, Loss: 0.428982
- Iter 655 / 659, Loss: 0.546046

* Train accuracy / confusion: 79.94% / [[7096, 1178, 267], [1202, 5700, 592], [275, 717, 4061]], 
* Val   accuracy / confusion: 77.54% / [[898, 138, 33], [209, 668, 61], [45, 107, 481]]

------------------------------ Epoch 076 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.412484
- Iter 262 / 659, Loss: 0.309956
- Iter 393 / 659, Loss: 0.542460
- Iter 524 / 659, Loss: 0.453071
- Iter 655 / 659, Loss: 0.575748

* Train accuracy / confusion: 80.34% / [[7096, 1176, 274], [1156, 5771, 561], [270, 708, 4076]], 
* Val   accuracy / confusion: 77.16% / [[930, 115, 24], [218, 624, 96], [65, 85, 483]]

------------------------------ Epoch 077 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.640009
- Iter 262 / 659, Loss: 0.367923
- Iter 393 / 659, Loss: 0.360320
- Iter 524 / 659, Loss: 0.538062
- Iter 655 / 659, Loss: 0.436444

* Train accuracy / confusion: 80.04% / [[7055, 1192, 295], [1180, 5740, 569], [268, 705, 4084]], 
* Val   accuracy / confusion: 76.02% / [[961, 84, 24], [276, 567, 95], [63, 91, 479]]

------------------------------ Epoch 078 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.409524
- Iter 262 / 659, Loss: 0.448698
- Iter 393 / 659, Loss: 0.738070
- Iter 524 / 659, Loss: 0.325473
- Iter 655 / 659, Loss: 0.565902

* Train accuracy / confusion: 80.70% / [[7137, 1158, 247], [1200, 5701, 589], [232, 645, 4179]], 
* Val   accuracy / confusion: 76.25% / [[880, 141, 48], [172, 624, 142], [53, 71, 509]]

------------------------------ Epoch 079 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.290177
- Iter 262 / 659, Loss: 0.315801
- Iter 393 / 659, Loss: 0.695136
- Iter 524 / 659, Loss: 0.514652
- Iter 655 / 659, Loss: 0.487389

* Train accuracy / confusion: 80.30% / [[7111, 1139, 293], [1206, 5693, 591], [258, 667, 4130]], 
* Val   accuracy / confusion: 77.69% / [[914, 114, 41], [187, 631, 120], [48, 79, 506]]

------------------------------ Epoch 080 / 200, Learning rate: 1.16e-02 ------------------------------
- Iter 131 / 659, Loss: 0.418186
- Iter 262 / 659, Loss: 0.508309
- Iter 393 / 659, Loss: 0.727217
- Iter 524 / 659, Loss: 0.656090
- Iter 655 / 659, Loss: 0.347694

* Train accuracy / confusion: 80.89% / [[7174, 1105, 268], [1202, 5712, 573], [235, 646, 4173]], 
* Val   accuracy / confusion: 78.18% / [[897, 138, 34], [177, 674, 87], [41, 99, 493]]

------------------------------ Epoch 081 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.330496
- Iter 262 / 659, Loss: 0.438590
- Iter 393 / 659, Loss: 0.549359
- Iter 524 / 659, Loss: 0.527727
- Iter 655 / 659, Loss: 0.615837

* Train accuracy / confusion: 82.52% / [[7305, 1005, 230], [1123, 5882, 485], [225, 619, 4214]], 
* Val   accuracy / confusion: 79.05% / [[895, 147, 27], [166, 692, 80], [43, 90, 500]]

------------------------------ Epoch 082 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.356967
- Iter 262 / 659, Loss: 0.449395
- Iter 393 / 659, Loss: 0.334034
- Iter 524 / 659, Loss: 0.284640
- Iter 655 / 659, Loss: 0.635039

* Train accuracy / confusion: 82.56% / [[7293, 1004, 245], [1144, 5873, 475], [231, 579, 4244]], 
* Val   accuracy / confusion: 78.94% / [[907, 127, 35], [181, 680, 77], [55, 81, 497]]

------------------------------ Epoch 083 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.311700
- Iter 262 / 659, Loss: 0.704976
- Iter 393 / 659, Loss: 0.617162
- Iter 524 / 659, Loss: 0.408443
- Iter 655 / 659, Loss: 0.196441

* Train accuracy / confusion: 83.45% / [[7322, 1006, 216], [1025, 6002, 463], [208, 572, 4274]], 
* Val   accuracy / confusion: 78.48% / [[879, 162, 28], [156, 704, 78], [44, 100, 489]]

------------------------------ Epoch 084 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.624659
- Iter 262 / 659, Loss: 0.386014
- Iter 393 / 659, Loss: 0.574377
- Iter 524 / 659, Loss: 0.659916
- Iter 655 / 659, Loss: 0.669281

* Train accuracy / confusion: 82.95% / [[7252, 1053, 241], [1072, 5950, 467], [188, 575, 4290]], 
* Val   accuracy / confusion: 77.99% / [[838, 193, 38], [131, 727, 80], [42, 97, 494]]

------------------------------ Epoch 085 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.338896
- Iter 262 / 659, Loss: 0.258480
- Iter 393 / 659, Loss: 0.291282
- Iter 524 / 659, Loss: 0.535605
- Iter 655 / 659, Loss: 0.425303

* Train accuracy / confusion: 83.89% / [[7345, 965, 230], [1019, 6010, 461], [209, 514, 4335]], 
* Val   accuracy / confusion: 79.09% / [[867, 166, 36], [129, 727, 82], [32, 107, 494]]

------------------------------ Epoch 086 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.298060
- Iter 262 / 659, Loss: 0.428506
- Iter 393 / 659, Loss: 0.331808
- Iter 524 / 659, Loss: 0.403480
- Iter 655 / 659, Loss: 0.348518

* Train accuracy / confusion: 84.12% / [[7386, 960, 198], [1000, 6037, 454], [173, 563, 4317]], 
* Val   accuracy / confusion: 78.48% / [[883, 155, 31], [164, 696, 78], [46, 94, 493]]

------------------------------ Epoch 087 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.613668
- Iter 262 / 659, Loss: 0.340891
- Iter 393 / 659, Loss: 0.451670
- Iter 524 / 659, Loss: 0.655351
- Iter 655 / 659, Loss: 0.188765

* Train accuracy / confusion: 84.02% / [[7370, 936, 237], [1024, 6004, 462], [180, 530, 4345]], 
* Val   accuracy / confusion: 78.22% / [[896, 156, 17], [169, 709, 60], [54, 119, 460]]

------------------------------ Epoch 088 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.355578
- Iter 262 / 659, Loss: 0.350713
- Iter 393 / 659, Loss: 0.623519
- Iter 524 / 659, Loss: 0.335755
- Iter 655 / 659, Loss: 0.312062

* Train accuracy / confusion: 84.11% / [[7320, 1017, 210], [993, 6046, 445], [191, 495, 4371]], 
* Val   accuracy / confusion: 78.18% / [[865, 175, 29], [156, 712, 70], [40, 106, 487]]

------------------------------ Epoch 089 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.165139
- Iter 262 / 659, Loss: 0.385738
- Iter 393 / 659, Loss: 0.362742
- Iter 524 / 659, Loss: 0.177861
- Iter 655 / 659, Loss: 0.323079

* Train accuracy / confusion: 84.47% / [[7377, 941, 225], [1005, 6058, 428], [177, 499, 4378]], 
* Val   accuracy / confusion: 79.13% / [[891, 145, 33], [160, 693, 85], [39, 89, 505]]

------------------------------ Epoch 090 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.325794
- Iter 262 / 659, Loss: 0.432139
- Iter 393 / 659, Loss: 0.229603
- Iter 524 / 659, Loss: 0.297495
- Iter 655 / 659, Loss: 0.328005

* Train accuracy / confusion: 84.10% / [[7408, 911, 225], [1040, 5974, 476], [184, 518, 4352]], 
* Val   accuracy / confusion: 79.05% / [[868, 162, 39], [142, 713, 83], [35, 92, 506]]

------------------------------ Epoch 091 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.316305
- Iter 262 / 659, Loss: 0.273367
- Iter 393 / 659, Loss: 0.238163
- Iter 524 / 659, Loss: 0.429591
- Iter 655 / 659, Loss: 0.468317

* Train accuracy / confusion: 84.02% / [[7382, 969, 192], [1071, 5971, 447], [160, 531, 4365]], 
* Val   accuracy / confusion: 79.58% / [[914, 136, 19], [175, 703, 60], [48, 101, 484]]

------------------------------ Epoch 092 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.483308
- Iter 262 / 659, Loss: 0.208814
- Iter 393 / 659, Loss: 0.415127
- Iter 524 / 659, Loss: 0.619081
- Iter 655 / 659, Loss: 0.401051

* Train accuracy / confusion: 84.39% / [[7362, 986, 198], [985, 6066, 443], [188, 492, 4368]], 
* Val   accuracy / confusion: 78.37% / [[886, 157, 26], [162, 707, 69], [54, 103, 476]]

------------------------------ Epoch 093 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.613350
- Iter 262 / 659, Loss: 0.327260
- Iter 393 / 659, Loss: 0.241320
- Iter 524 / 659, Loss: 0.299928
- Iter 655 / 659, Loss: 0.387440

* Train accuracy / confusion: 84.30% / [[7371, 969, 204], [1008, 6016, 464], [167, 499, 4390]], 
* Val   accuracy / confusion: 79.43% / [[887, 146, 36], [153, 697, 88], [37, 83, 513]]

------------------------------ Epoch 094 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.435460
- Iter 262 / 659, Loss: 0.205178
- Iter 393 / 659, Loss: 0.366064
- Iter 524 / 659, Loss: 0.403256
- Iter 655 / 659, Loss: 0.291698

* Train accuracy / confusion: 84.72% / [[7390, 946, 206], [961, 6084, 442], [192, 476, 4391]], 
* Val   accuracy / confusion: 79.43% / [[876, 166, 27], [146, 728, 64], [42, 98, 493]]

------------------------------ Epoch 095 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.439475
- Iter 262 / 659, Loss: 0.317620
- Iter 393 / 659, Loss: 0.357873
- Iter 524 / 659, Loss: 0.243198
- Iter 655 / 659, Loss: 0.462737

* Train accuracy / confusion: 84.27% / [[7394, 929, 225], [1016, 6009, 461], [191, 496, 4367]], 
* Val   accuracy / confusion: 79.17% / [[890, 154, 25], [166, 709, 63], [46, 96, 491]]

------------------------------ Epoch 096 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.243421
- Iter 262 / 659, Loss: 0.424854
- Iter 393 / 659, Loss: 0.447904
- Iter 524 / 659, Loss: 0.305958
- Iter 655 / 659, Loss: 0.220898

* Train accuracy / confusion: 84.30% / [[7353, 987, 203], [1023, 6021, 445], [179, 473, 4404]], 
* Val   accuracy / confusion: 79.51% / [[880, 151, 38], [160, 709, 69], [39, 84, 510]]

------------------------------ Epoch 097 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.365773
- Iter 262 / 659, Loss: 0.371168
- Iter 393 / 659, Loss: 0.489809
- Iter 524 / 659, Loss: 0.384026
- Iter 655 / 659, Loss: 0.262502

* Train accuracy / confusion: 84.43% / [[7380, 947, 216], [991, 6052, 450], [188, 491, 4373]], 
* Val   accuracy / confusion: 78.60% / [[880, 174, 15], [162, 719, 57], [42, 115, 476]]

------------------------------ Epoch 098 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.459146
- Iter 262 / 659, Loss: 0.178547
- Iter 393 / 659, Loss: 0.330279
- Iter 524 / 659, Loss: 0.717323
- Iter 655 / 659, Loss: 0.263518

* Train accuracy / confusion: 84.26% / [[7378, 943, 225], [977, 6043, 467], [203, 504, 4348]], 
* Val   accuracy / confusion: 78.30% / [[867, 166, 36], [161, 693, 84], [41, 85, 507]]

------------------------------ Epoch 099 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.417864
- Iter 262 / 659, Loss: 0.462008
- Iter 393 / 659, Loss: 0.308956
- Iter 524 / 659, Loss: 0.290172
- Iter 655 / 659, Loss: 0.328416

* Train accuracy / confusion: 84.54% / [[7371, 958, 213], [980, 6067, 445], [190, 475, 4389]], 
* Val   accuracy / confusion: 78.60% / [[859, 176, 34], [138, 728, 72], [37, 108, 488]]

------------------------------ Epoch 100 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.485649
- Iter 262 / 659, Loss: 0.319199
- Iter 393 / 659, Loss: 0.155607
- Iter 524 / 659, Loss: 0.317680
- Iter 655 / 659, Loss: 0.505363

* Train accuracy / confusion: 85.02% / [[7423, 908, 215], [973, 6122, 390], [202, 472, 4383]], 
* Val   accuracy / confusion: 79.28% / [[889, 150, 30], [168, 704, 66], [39, 94, 500]]

------------------------------ Epoch 101 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.475221
- Iter 262 / 659, Loss: 0.501342
- Iter 393 / 659, Loss: 0.406969
- Iter 524 / 659, Loss: 0.339800
- Iter 655 / 659, Loss: 0.383804

* Train accuracy / confusion: 84.72% / [[7370, 963, 213], [1002, 6063, 423], [164, 457, 4433]], 
* Val   accuracy / confusion: 79.51% / [[859, 175, 35], [128, 738, 72], [38, 93, 502]]

------------------------------ Epoch 102 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.404539
- Iter 262 / 659, Loss: 0.247799
- Iter 393 / 659, Loss: 0.256906
- Iter 524 / 659, Loss: 0.237907
- Iter 655 / 659, Loss: 0.236026

* Train accuracy / confusion: 84.81% / [[7368, 980, 197], [941, 6120, 431], [189, 465, 4397]], 
* Val   accuracy / confusion: 79.05% / [[861, 178, 30], [144, 723, 71], [44, 86, 503]]

------------------------------ Epoch 103 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.688335
- Iter 262 / 659, Loss: 0.272115
- Iter 393 / 659, Loss: 0.375402
- Iter 524 / 659, Loss: 0.483102
- Iter 655 / 659, Loss: 0.245531

* Train accuracy / confusion: 84.76% / [[7411, 938, 193], [948, 6078, 461], [199, 475, 4385]], 
* Val   accuracy / confusion: 77.88% / [[883, 166, 20], [172, 704, 62], [55, 109, 469]]

------------------------------ Epoch 104 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.369651
- Iter 262 / 659, Loss: 0.234442
- Iter 393 / 659, Loss: 0.349748
- Iter 524 / 659, Loss: 0.332613
- Iter 655 / 659, Loss: 0.266211

* Train accuracy / confusion: 84.94% / [[7403, 941, 201], [956, 6107, 424], [151, 503, 4402]], 
* Val   accuracy / confusion: 79.47% / [[873, 175, 21], [141, 751, 46], [49, 110, 474]]

------------------------------ Epoch 105 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.227561
- Iter 262 / 659, Loss: 0.330653
- Iter 393 / 659, Loss: 0.376033
- Iter 524 / 659, Loss: 0.335423
- Iter 655 / 659, Loss: 0.308700

* Train accuracy / confusion: 84.74% / [[7394, 947, 209], [972, 6088, 428], [166, 497, 4387]], 
* Val   accuracy / confusion: 79.02% / [[891, 152, 26], [151, 724, 63], [58, 104, 471]]

------------------------------ Epoch 106 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.259596
- Iter 262 / 659, Loss: 0.292272
- Iter 393 / 659, Loss: 0.229132
- Iter 524 / 659, Loss: 0.311662
- Iter 655 / 659, Loss: 0.413300

* Train accuracy / confusion: 84.88% / [[7433, 925, 183], [996, 6048, 449], [174, 461, 4419]], 
* Val   accuracy / confusion: 79.43% / [[873, 161, 35], [141, 725, 72], [39, 95, 499]]

------------------------------ Epoch 107 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.447076
- Iter 262 / 659, Loss: 0.286265
- Iter 393 / 659, Loss: 0.308271
- Iter 524 / 659, Loss: 0.418156
- Iter 655 / 659, Loss: 0.345257

* Train accuracy / confusion: 85.07% / [[7409, 939, 197], [951, 6097, 443], [170, 448, 4434]], 
* Val   accuracy / confusion: 78.67% / [[882, 167, 20], [148, 732, 58], [45, 125, 463]]

------------------------------ Epoch 108 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.209520
- Iter 262 / 659, Loss: 0.412357
- Iter 393 / 659, Loss: 0.143686
- Iter 524 / 659, Loss: 0.331879
- Iter 655 / 659, Loss: 0.398030

* Train accuracy / confusion: 85.12% / [[7373, 967, 204], [933, 6155, 402], [174, 457, 4423]], 
* Val   accuracy / confusion: 79.05% / [[876, 169, 24], [154, 721, 63], [42, 101, 490]]

------------------------------ Epoch 109 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.215389
- Iter 262 / 659, Loss: 0.497265
- Iter 393 / 659, Loss: 0.227304
- Iter 524 / 659, Loss: 0.417317
- Iter 655 / 659, Loss: 0.345071

* Train accuracy / confusion: 85.25% / [[7469, 877, 197], [907, 6126, 461], [168, 501, 4382]], 
* Val   accuracy / confusion: 79.32% / [[883, 154, 32], [142, 722, 74], [49, 95, 489]]

------------------------------ Epoch 110 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.170183
- Iter 262 / 659, Loss: 0.568436
- Iter 393 / 659, Loss: 0.276661
- Iter 524 / 659, Loss: 0.526787
- Iter 655 / 659, Loss: 0.701148

* Train accuracy / confusion: 85.20% / [[7399, 939, 209], [888, 6157, 444], [167, 473, 4412]], 
* Val   accuracy / confusion: 79.20% / [[867, 174, 28], [132, 745, 61], [42, 112, 479]]

------------------------------ Epoch 111 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.381759
- Iter 262 / 659, Loss: 0.414793
- Iter 393 / 659, Loss: 0.222335
- Iter 524 / 659, Loss: 0.498635
- Iter 655 / 659, Loss: 0.235579

* Train accuracy / confusion: 85.14% / [[7408, 914, 221], [918, 6114, 460], [141, 479, 4433]], 
* Val   accuracy / confusion: 77.80% / [[879, 160, 30], [175, 691, 72], [59, 90, 484]]

------------------------------ Epoch 112 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.227028
- Iter 262 / 659, Loss: 0.473946
- Iter 393 / 659, Loss: 0.313702
- Iter 524 / 659, Loss: 0.488909
- Iter 655 / 659, Loss: 0.483715

* Train accuracy / confusion: 85.01% / [[7402, 947, 189], [905, 6119, 470], [175, 475, 4406]], 
* Val   accuracy / confusion: 78.90% / [[876, 171, 22], [155, 720, 63], [40, 106, 487]]

------------------------------ Epoch 113 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.320436
- Iter 262 / 659, Loss: 0.636166
- Iter 393 / 659, Loss: 0.343750
- Iter 524 / 659, Loss: 0.312931
- Iter 655 / 659, Loss: 0.428708

* Train accuracy / confusion: 85.11% / [[7394, 940, 211], [918, 6133, 435], [176, 459, 4422]], 
* Val   accuracy / confusion: 79.13% / [[912, 146, 11], [167, 708, 63], [50, 114, 469]]

------------------------------ Epoch 114 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.309147
- Iter 262 / 659, Loss: 0.360086
- Iter 393 / 659, Loss: 0.304053
- Iter 524 / 659, Loss: 0.500522
- Iter 655 / 659, Loss: 0.145561

* Train accuracy / confusion: 85.51% / [[7423, 909, 208], [894, 6159, 441], [151, 452, 4451]], 
* Val   accuracy / confusion: 79.20% / [[889, 159, 21], [159, 716, 63], [48, 99, 486]]

------------------------------ Epoch 115 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.295020
- Iter 262 / 659, Loss: 0.279868
- Iter 393 / 659, Loss: 0.373536
- Iter 524 / 659, Loss: 0.380446
- Iter 655 / 659, Loss: 0.367096

* Train accuracy / confusion: 84.99% / [[7414, 946, 184], [958, 6074, 459], [147, 472, 4434]], 
* Val   accuracy / confusion: 78.14% / [[868, 168, 33], [156, 703, 79], [40, 101, 492]]

------------------------------ Epoch 116 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.469281
- Iter 262 / 659, Loss: 0.660717
- Iter 393 / 659, Loss: 0.304196
- Iter 524 / 659, Loss: 0.381168
- Iter 655 / 659, Loss: 0.267403

* Train accuracy / confusion: 85.57% / [[7450, 910, 185], [907, 6151, 436], [156, 448, 4445]], 
* Val   accuracy / confusion: 79.09% / [[902, 140, 27], [173, 682, 83], [39, 90, 504]]

------------------------------ Epoch 117 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.239007
- Iter 262 / 659, Loss: 0.400145
- Iter 393 / 659, Loss: 0.440974
- Iter 524 / 659, Loss: 0.327883
- Iter 655 / 659, Loss: 0.577752

* Train accuracy / confusion: 85.29% / [[7400, 919, 223], [919, 6131, 440], [173, 428, 4455]], 
* Val   accuracy / confusion: 79.17% / [[896, 148, 25], [164, 702, 72], [45, 96, 492]]

------------------------------ Epoch 118 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.321702
- Iter 262 / 659, Loss: 0.414624
- Iter 393 / 659, Loss: 0.359876
- Iter 524 / 659, Loss: 0.611829
- Iter 655 / 659, Loss: 0.637878

* Train accuracy / confusion: 85.14% / [[7402, 931, 213], [931, 6133, 426], [164, 468, 4420]], 
* Val   accuracy / confusion: 77.80% / [[838, 196, 35], [127, 713, 98], [36, 94, 503]]

------------------------------ Epoch 119 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.308088
- Iter 262 / 659, Loss: 0.676885
- Iter 393 / 659, Loss: 0.564759
- Iter 524 / 659, Loss: 0.312389
- Iter 655 / 659, Loss: 0.517559

* Train accuracy / confusion: 85.35% / [[7421, 923, 197], [926, 6145, 422], [174, 448, 4432]], 
* Val   accuracy / confusion: 77.88% / [[868, 171, 30], [170, 691, 77], [43, 93, 497]]

------------------------------ Epoch 120 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.258946
- Iter 262 / 659, Loss: 0.523521
- Iter 393 / 659, Loss: 0.308480
- Iter 524 / 659, Loss: 0.424127
- Iter 655 / 659, Loss: 0.312874

* Train accuracy / confusion: 85.01% / [[7408, 936, 197], [985, 6099, 410], [189, 444, 4420]], 
* Val   accuracy / confusion: 78.67% / [[875, 169, 25], [145, 725, 68], [42, 114, 477]]

------------------------------ Epoch 121 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.338089
- Iter 262 / 659, Loss: 0.522153
- Iter 393 / 659, Loss: 0.196518
- Iter 524 / 659, Loss: 0.382020
- Iter 655 / 659, Loss: 0.376511

* Train accuracy / confusion: 85.47% / [[7448, 890, 206], [907, 6156, 426], [149, 486, 4420]], 
* Val   accuracy / confusion: 78.83% / [[883, 153, 33], [143, 732, 63], [56, 111, 466]]

------------------------------ Epoch 122 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.401633
- Iter 262 / 659, Loss: 0.297807
- Iter 393 / 659, Loss: 0.305858
- Iter 524 / 659, Loss: 0.504234
- Iter 655 / 659, Loss: 0.403956

* Train accuracy / confusion: 85.50% / [[7444, 893, 208], [884, 6168, 434], [190, 449, 4418]], 
* Val   accuracy / confusion: 78.71% / [[825, 207, 37], [123, 754, 61], [36, 98, 499]]

------------------------------ Epoch 123 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.356905
- Iter 262 / 659, Loss: 0.152926
- Iter 393 / 659, Loss: 0.236705
- Iter 524 / 659, Loss: 0.225409
- Iter 655 / 659, Loss: 0.316412

* Train accuracy / confusion: 85.68% / [[7481, 862, 198], [924, 6167, 399], [164, 472, 4421]], 
* Val   accuracy / confusion: 79.58% / [[898, 148, 23], [163, 694, 81], [36, 88, 509]]

------------------------------ Epoch 124 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.401250
- Iter 262 / 659, Loss: 0.699118
- Iter 393 / 659, Loss: 0.246841
- Iter 524 / 659, Loss: 0.387476
- Iter 655 / 659, Loss: 0.551347

* Train accuracy / confusion: 85.48% / [[7425, 908, 210], [933, 6143, 412], [185, 415, 4457]], 
* Val   accuracy / confusion: 78.64% / [[886, 156, 27], [169, 694, 75], [44, 93, 496]]

------------------------------ Epoch 125 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.505891
- Iter 262 / 659, Loss: 0.414806
- Iter 393 / 659, Loss: 0.351498
- Iter 524 / 659, Loss: 0.460222
- Iter 655 / 659, Loss: 0.378119

* Train accuracy / confusion: 85.01% / [[7392, 938, 212], [924, 6115, 449], [177, 461, 4420]], 
* Val   accuracy / confusion: 79.32% / [[862, 167, 40], [130, 725, 83], [34, 92, 507]]

------------------------------ Epoch 126 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.309082
- Iter 262 / 659, Loss: 0.562581
- Iter 393 / 659, Loss: 0.341947
- Iter 524 / 659, Loss: 0.506816
- Iter 655 / 659, Loss: 0.556278

* Train accuracy / confusion: 85.53% / [[7426, 898, 221], [894, 6163, 433], [181, 424, 4448]], 
* Val   accuracy / confusion: 79.24% / [[903, 140, 26], [167, 683, 88], [36, 91, 506]]

------------------------------ Epoch 127 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.222722
- Iter 262 / 659, Loss: 0.367503
- Iter 393 / 659, Loss: 0.381836
- Iter 524 / 659, Loss: 0.287992
- Iter 655 / 659, Loss: 0.509498

* Train accuracy / confusion: 85.29% / [[7419, 929, 198], [917, 6124, 449], [159, 450, 4443]], 
* Val   accuracy / confusion: 79.55% / [[909, 138, 22], [158, 709, 71], [49, 102, 482]]

------------------------------ Epoch 128 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.286672
- Iter 262 / 659, Loss: 0.406908
- Iter 393 / 659, Loss: 0.276792
- Iter 524 / 659, Loss: 0.452187
- Iter 655 / 659, Loss: 0.462419

* Train accuracy / confusion: 85.20% / [[7455, 894, 194], [959, 6066, 462], [170, 441, 4447]], 
* Val   accuracy / confusion: 78.64% / [[906, 136, 27], [183, 669, 86], [54, 78, 501]]

------------------------------ Epoch 129 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.222438
- Iter 262 / 659, Loss: 0.318948
- Iter 393 / 659, Loss: 0.307071
- Iter 524 / 659, Loss: 0.328876
- Iter 655 / 659, Loss: 0.236006

* Train accuracy / confusion: 85.49% / [[7433, 899, 213], [914, 6161, 412], [163, 459, 4434]], 
* Val   accuracy / confusion: 79.20% / [[909, 133, 27], [181, 673, 84], [50, 74, 509]]

------------------------------ Epoch 130 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.327911
- Iter 262 / 659, Loss: 0.365125
- Iter 393 / 659, Loss: 0.250106
- Iter 524 / 659, Loss: 0.507980
- Iter 655 / 659, Loss: 0.517859

* Train accuracy / confusion: 85.29% / [[7452, 891, 197], [921, 6132, 442], [173, 478, 4402]], 
* Val   accuracy / confusion: 78.41% / [[869, 174, 26], [144, 727, 67], [51, 108, 474]]

------------------------------ Epoch 131 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.476552
- Iter 262 / 659, Loss: 0.333861
- Iter 393 / 659, Loss: 0.348050
- Iter 524 / 659, Loss: 0.500093
- Iter 655 / 659, Loss: 0.323154

* Train accuracy / confusion: 85.39% / [[7444, 894, 207], [898, 6147, 444], [181, 456, 4417]], 
* Val   accuracy / confusion: 79.05% / [[886, 152, 31], [139, 733, 66], [43, 122, 468]]

------------------------------ Epoch 132 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.393767
- Iter 262 / 659, Loss: 0.338534
- Iter 393 / 659, Loss: 0.244888
- Iter 524 / 659, Loss: 0.612631
- Iter 655 / 659, Loss: 0.321949

* Train accuracy / confusion: 85.67% / [[7446, 895, 201], [901, 6168, 425], [172, 427, 4453]], 
* Val   accuracy / confusion: 79.17% / [[893, 154, 22], [166, 701, 71], [40, 97, 496]]

------------------------------ Epoch 133 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.372383
- Iter 262 / 659, Loss: 0.254023
- Iter 393 / 659, Loss: 0.247280
- Iter 524 / 659, Loss: 0.411070
- Iter 655 / 659, Loss: 0.217532

* Train accuracy / confusion: 85.83% / [[7468, 887, 188], [897, 6178, 421], [183, 412, 4454]], 
* Val   accuracy / confusion: 78.64% / [[885, 158, 26], [162, 691, 85], [43, 90, 500]]

------------------------------ Epoch 134 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.334615
- Iter 262 / 659, Loss: 0.340723
- Iter 393 / 659, Loss: 0.375273
- Iter 524 / 659, Loss: 0.267772
- Iter 655 / 659, Loss: 0.404637

* Train accuracy / confusion: 86.16% / [[7446, 888, 210], [871, 6243, 379], [142, 428, 4481]], 
* Val   accuracy / confusion: 79.13% / [[872, 168, 29], [140, 745, 53], [47, 114, 472]]

------------------------------ Epoch 135 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.285021
- Iter 262 / 659, Loss: 0.337071
- Iter 393 / 659, Loss: 0.347900
- Iter 524 / 659, Loss: 0.325692
- Iter 655 / 659, Loss: 0.285627

* Train accuracy / confusion: 85.52% / [[7459, 857, 226], [923, 6133, 430], [173, 444, 4443]], 
* Val   accuracy / confusion: 77.99% / [[853, 186, 30], [140, 730, 68], [36, 121, 476]]

------------------------------ Epoch 136 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.527149
- Iter 262 / 659, Loss: 0.545971
- Iter 393 / 659, Loss: 0.325674
- Iter 524 / 659, Loss: 0.295634
- Iter 655 / 659, Loss: 0.199206

* Train accuracy / confusion: 85.91% / [[7470, 875, 195], [900, 6180, 413], [142, 446, 4467]], 
* Val   accuracy / confusion: 79.13% / [[855, 180, 34], [139, 711, 88], [29, 81, 523]]

------------------------------ Epoch 137 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.319668
- Iter 262 / 659, Loss: 0.277230
- Iter 393 / 659, Loss: 0.268142
- Iter 524 / 659, Loss: 0.479699
- Iter 655 / 659, Loss: 0.355516

* Train accuracy / confusion: 86.06% / [[7453, 889, 200], [855, 6231, 407], [176, 413, 4464]], 
* Val   accuracy / confusion: 78.94% / [[889, 153, 27], [172, 703, 63], [47, 94, 492]]

------------------------------ Epoch 138 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.342688
- Iter 262 / 659, Loss: 0.343669
- Iter 393 / 659, Loss: 0.252680
- Iter 524 / 659, Loss: 0.233057
- Iter 655 / 659, Loss: 0.443758

* Train accuracy / confusion: 85.86% / [[7483, 865, 195], [886, 6179, 425], [169, 442, 4444]], 
* Val   accuracy / confusion: 78.71% / [[852, 181, 36], [144, 717, 77], [35, 89, 509]]

------------------------------ Epoch 139 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.563897
- Iter 262 / 659, Loss: 0.618041
- Iter 393 / 659, Loss: 0.272405
- Iter 524 / 659, Loss: 0.212249
- Iter 655 / 659, Loss: 0.259412

* Train accuracy / confusion: 85.66% / [[7455, 900, 189], [905, 6175, 412], [176, 442, 4434]], 
* Val   accuracy / confusion: 79.55% / [[888, 151, 30], [160, 716, 62], [39, 98, 496]]

------------------------------ Epoch 140 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.374912
- Iter 262 / 659, Loss: 0.265959
- Iter 393 / 659, Loss: 0.198205
- Iter 524 / 659, Loss: 0.356325
- Iter 655 / 659, Loss: 0.622348

* Train accuracy / confusion: 86.08% / [[7469, 876, 199], [875, 6207, 409], [162, 415, 4476]], 
* Val   accuracy / confusion: 79.02% / [[855, 180, 34], [136, 744, 58], [37, 109, 487]]

------------------------------ Epoch 141 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.322115
- Iter 262 / 659, Loss: 0.318502
- Iter 393 / 659, Loss: 0.188305
- Iter 524 / 659, Loss: 0.213894
- Iter 655 / 659, Loss: 0.321508

* Train accuracy / confusion: 85.73% / [[7465, 896, 181], [872, 6192, 429], [163, 468, 4422]], 
* Val   accuracy / confusion: 79.92% / [[893, 142, 34], [163, 708, 67], [33, 91, 509]]

------------------------------ Epoch 142 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.462213
- Iter 262 / 659, Loss: 0.637429
- Iter 393 / 659, Loss: 0.354662
- Iter 524 / 659, Loss: 0.304570
- Iter 655 / 659, Loss: 0.308332

* Train accuracy / confusion: 85.84% / [[7449, 894, 202], [936, 6171, 383], [149, 422, 4482]], 
* Val   accuracy / confusion: 79.09% / [[911, 125, 33], [166, 694, 78], [46, 104, 483]]

------------------------------ Epoch 143 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.401690
- Iter 262 / 659, Loss: 0.261741
- Iter 393 / 659, Loss: 0.394941
- Iter 524 / 659, Loss: 0.380084
- Iter 655 / 659, Loss: 0.362995

* Train accuracy / confusion: 85.94% / [[7448, 890, 206], [886, 6218, 385], [171, 428, 4456]], 
* Val   accuracy / confusion: 79.47% / [[908, 140, 21], [166, 715, 57], [52, 106, 475]]

------------------------------ Epoch 144 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.186584
- Iter 262 / 659, Loss: 0.424191
- Iter 393 / 659, Loss: 0.439855
- Iter 524 / 659, Loss: 0.369838
- Iter 655 / 659, Loss: 0.342658

* Train accuracy / confusion: 85.63% / [[7461, 884, 195], [882, 6166, 445], [170, 455, 4430]], 
* Val   accuracy / confusion: 78.75% / [[905, 131, 33], [175, 679, 84], [44, 94, 495]]

------------------------------ Epoch 145 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.536808
- Iter 262 / 659, Loss: 0.423108
- Iter 393 / 659, Loss: 0.340314
- Iter 524 / 659, Loss: 0.297941
- Iter 655 / 659, Loss: 0.183319

* Train accuracy / confusion: 86.05% / [[7483, 872, 187], [907, 6200, 387], [170, 419, 4463]], 
* Val   accuracy / confusion: 80.30% / [[901, 136, 32], [156, 699, 83], [49, 64, 520]]

------------------------------ Epoch 146 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.306319
- Iter 262 / 659, Loss: 0.355027
- Iter 393 / 659, Loss: 0.218524
- Iter 524 / 659, Loss: 0.306000
- Iter 655 / 659, Loss: 0.682861

* Train accuracy / confusion: 85.55% / [[7457, 893, 195], [909, 6134, 448], [158, 445, 4449]], 
* Val   accuracy / confusion: 80.68% / [[881, 159, 29], [129, 738, 71], [28, 94, 511]]

------------------------------ Epoch 147 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.324467
- Iter 262 / 659, Loss: 0.297042
- Iter 393 / 659, Loss: 0.434207
- Iter 524 / 659, Loss: 0.241453
- Iter 655 / 659, Loss: 0.295307

* Train accuracy / confusion: 86.00% / [[7482, 857, 202], [881, 6197, 412], [176, 425, 4456]], 
* Val   accuracy / confusion: 80.34% / [[906, 134, 29], [152, 713, 73], [51, 80, 502]]

------------------------------ Epoch 148 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.211992
- Iter 262 / 659, Loss: 0.265632
- Iter 393 / 659, Loss: 0.282608
- Iter 524 / 659, Loss: 0.470605
- Iter 655 / 659, Loss: 0.321100

* Train accuracy / confusion: 85.95% / [[7491, 858, 193], [896, 6158, 435], [176, 404, 4477]], 
* Val   accuracy / confusion: 80.23% / [[912, 134, 23], [170, 709, 59], [49, 87, 497]]

------------------------------ Epoch 149 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.354888
- Iter 262 / 659, Loss: 0.443978
- Iter 393 / 659, Loss: 0.452237
- Iter 524 / 659, Loss: 0.402328
- Iter 655 / 659, Loss: 0.356467

* Train accuracy / confusion: 86.25% / [[7492, 856, 191], [898, 6195, 399], [151, 404, 4502]], 
* Val   accuracy / confusion: 79.32% / [[830, 197, 42], [122, 753, 63], [29, 93, 511]]

------------------------------ Epoch 150 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.665207
- Iter 262 / 659, Loss: 0.280903
- Iter 393 / 659, Loss: 0.167220
- Iter 524 / 659, Loss: 0.242922
- Iter 655 / 659, Loss: 0.487508

* Train accuracy / confusion: 86.06% / [[7459, 891, 194], [828, 6256, 409], [181, 436, 4434]], 
* Val   accuracy / confusion: 78.79% / [[920, 134, 15], [172, 693, 73], [58, 108, 467]]

------------------------------ Epoch 151 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.359523
- Iter 262 / 659, Loss: 0.252736
- Iter 393 / 659, Loss: 0.505392
- Iter 524 / 659, Loss: 0.277328
- Iter 655 / 659, Loss: 0.246936

* Train accuracy / confusion: 86.15% / [[7478, 872, 191], [877, 6229, 384], [160, 437, 4460]], 
* Val   accuracy / confusion: 80.04% / [[897, 140, 32], [164, 702, 72], [39, 80, 514]]

------------------------------ Epoch 152 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.198779
- Iter 262 / 659, Loss: 0.480135
- Iter 393 / 659, Loss: 0.395181
- Iter 524 / 659, Loss: 0.193829
- Iter 655 / 659, Loss: 0.303448

* Train accuracy / confusion: 86.16% / [[7491, 859, 191], [898, 6200, 392], [155, 423, 4479]], 
* Val   accuracy / confusion: 78.98% / [[878, 167, 24], [150, 709, 79], [44, 91, 498]]

------------------------------ Epoch 153 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.293088
- Iter 262 / 659, Loss: 0.192143
- Iter 393 / 659, Loss: 0.483393
- Iter 524 / 659, Loss: 0.141507
- Iter 655 / 659, Loss: 0.358966

* Train accuracy / confusion: 86.08% / [[7472, 866, 206], [877, 6198, 413], [173, 400, 4483]], 
* Val   accuracy / confusion: 79.58% / [[923, 118, 28], [177, 695, 66], [53, 97, 483]]

------------------------------ Epoch 154 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.481287
- Iter 262 / 659, Loss: 0.240935
- Iter 393 / 659, Loss: 0.356937
- Iter 524 / 659, Loss: 0.528707
- Iter 655 / 659, Loss: 0.341073

* Train accuracy / confusion: 85.85% / [[7470, 880, 196], [944, 6156, 388], [149, 427, 4478]], 
* Val   accuracy / confusion: 79.32% / [[887, 157, 25], [153, 719, 66], [46, 99, 488]]

------------------------------ Epoch 155 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.270699
- Iter 262 / 659, Loss: 0.288394
- Iter 393 / 659, Loss: 0.394013
- Iter 524 / 659, Loss: 0.237998
- Iter 655 / 659, Loss: 0.364006

* Train accuracy / confusion: 86.17% / [[7490, 877, 174], [888, 6212, 393], [153, 431, 4470]], 
* Val   accuracy / confusion: 79.05% / [[886, 162, 21], [162, 714, 62], [47, 99, 487]]

------------------------------ Epoch 156 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.732397
- Iter 262 / 659, Loss: 0.327632
- Iter 393 / 659, Loss: 0.252166
- Iter 524 / 659, Loss: 0.466669
- Iter 655 / 659, Loss: 0.486463

* Train accuracy / confusion: 85.78% / [[7446, 881, 213], [862, 6202, 428], [169, 446, 4441]], 
* Val   accuracy / confusion: 78.90% / [[876, 143, 50], [141, 681, 116], [35, 72, 526]]

------------------------------ Epoch 157 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.689143
- Iter 262 / 659, Loss: 0.421752
- Iter 393 / 659, Loss: 0.365982
- Iter 524 / 659, Loss: 0.411721
- Iter 655 / 659, Loss: 0.324479

* Train accuracy / confusion: 86.21% / [[7503, 858, 182], [862, 6214, 415], [153, 438, 4463]], 
* Val   accuracy / confusion: 79.17% / [[875, 168, 26], [141, 733, 64], [37, 114, 482]]

------------------------------ Epoch 158 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.224541
- Iter 262 / 659, Loss: 0.404851
- Iter 393 / 659, Loss: 0.250635
- Iter 524 / 659, Loss: 0.509449
- Iter 655 / 659, Loss: 0.272223

* Train accuracy / confusion: 86.17% / [[7506, 836, 204], [878, 6203, 407], [165, 427, 4462]], 
* Val   accuracy / confusion: 79.05% / [[885, 147, 37], [158, 698, 82], [38, 91, 504]]

------------------------------ Epoch 159 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.469591
- Iter 262 / 659, Loss: 0.260266
- Iter 393 / 659, Loss: 0.482767
- Iter 524 / 659, Loss: 0.230323
- Iter 655 / 659, Loss: 0.275303

* Train accuracy / confusion: 85.66% / [[7449, 913, 185], [853, 6180, 454], [161, 457, 4436]], 
* Val   accuracy / confusion: 78.41% / [[883, 150, 36], [148, 693, 97], [41, 98, 494]]

------------------------------ Epoch 160 / 200, Learning rate: 1.16e-03 ------------------------------
- Iter 131 / 659, Loss: 0.441094
- Iter 262 / 659, Loss: 0.200766
- Iter 393 / 659, Loss: 0.355868
- Iter 524 / 659, Loss: 0.422328
- Iter 655 / 659, Loss: 0.251046

* Train accuracy / confusion: 86.10% / [[7483, 855, 203], [866, 6211, 415], [163, 429, 4463]], 
* Val   accuracy / confusion: 80.30% / [[925, 124, 20], [165, 699, 74], [47, 90, 496]]

------------------------------ Epoch 161 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.438603
- Iter 262 / 659, Loss: 0.551265
- Iter 393 / 659, Loss: 0.326462
- Iter 524 / 659, Loss: 0.356259
- Iter 655 / 659, Loss: 0.215697

* Train accuracy / confusion: 86.36% / [[7557, 812, 175], [884, 6191, 416], [154, 435, 4464]], 
* Val   accuracy / confusion: 80.45% / [[910, 138, 21], [158, 707, 73], [42, 84, 507]]

------------------------------ Epoch 162 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.254836
- Iter 262 / 659, Loss: 0.338717
- Iter 393 / 659, Loss: 0.390423
- Iter 524 / 659, Loss: 0.481480
- Iter 655 / 659, Loss: 0.364552

* Train accuracy / confusion: 86.31% / [[7494, 852, 199], [848, 6239, 400], [151, 437, 4468]], 
* Val   accuracy / confusion: 80.57% / [[919, 126, 24], [159, 713, 66], [46, 92, 495]]

------------------------------ Epoch 163 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.445107
- Iter 262 / 659, Loss: 0.462600
- Iter 393 / 659, Loss: 0.342503
- Iter 524 / 659, Loss: 0.320313
- Iter 655 / 659, Loss: 0.321147

* Train accuracy / confusion: 86.19% / [[7505, 862, 176], [871, 6215, 402], [142, 459, 4456]], 
* Val   accuracy / confusion: 80.19% / [[914, 128, 27], [168, 711, 59], [47, 94, 492]]

------------------------------ Epoch 164 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.217275
- Iter 262 / 659, Loss: 0.469797
- Iter 393 / 659, Loss: 0.306088
- Iter 524 / 659, Loss: 0.653131
- Iter 655 / 659, Loss: 0.225779

* Train accuracy / confusion: 86.17% / [[7475, 876, 194], [865, 6226, 400], [166, 415, 4471]], 
* Val   accuracy / confusion: 79.70% / [[900, 146, 23], [156, 721, 61], [52, 98, 483]]

------------------------------ Epoch 165 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.309668
- Iter 262 / 659, Loss: 0.544146
- Iter 393 / 659, Loss: 0.436920
- Iter 524 / 659, Loss: 0.426891
- Iter 655 / 659, Loss: 0.427951

* Train accuracy / confusion: 86.27% / [[7489, 857, 197], [854, 6242, 395], [159, 433, 4462]], 
* Val   accuracy / confusion: 78.86% / [[898, 142, 29], [177, 680, 81], [37, 92, 504]]

------------------------------ Epoch 166 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.213584
- Iter 262 / 659, Loss: 0.441945
- Iter 393 / 659, Loss: 0.213980
- Iter 524 / 659, Loss: 0.490809
- Iter 655 / 659, Loss: 0.337236

* Train accuracy / confusion: 86.16% / [[7473, 871, 198], [891, 6201, 402], [134, 423, 4495]], 
* Val   accuracy / confusion: 79.58% / [[902, 148, 19], [150, 714, 74], [39, 109, 485]]

------------------------------ Epoch 167 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.229846
- Iter 262 / 659, Loss: 0.234689
- Iter 393 / 659, Loss: 0.208388
- Iter 524 / 659, Loss: 0.399418
- Iter 655 / 659, Loss: 0.261189

* Train accuracy / confusion: 86.31% / [[7501, 865, 176], [892, 6214, 385], [163, 405, 4487]], 
* Val   accuracy / confusion: 79.96% / [[909, 132, 28], [165, 702, 71], [55, 78, 500]]

------------------------------ Epoch 168 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.165431
- Iter 262 / 659, Loss: 0.282940
- Iter 393 / 659, Loss: 0.674494
- Iter 524 / 659, Loss: 0.617500
- Iter 655 / 659, Loss: 0.284169

* Train accuracy / confusion: 86.58% / [[7548, 827, 171], [815, 6248, 427], [162, 429, 4461]], 
* Val   accuracy / confusion: 79.05% / [[876, 162, 31], [156, 707, 75], [35, 94, 504]]

------------------------------ Epoch 169 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.339801
- Iter 262 / 659, Loss: 0.228746
- Iter 393 / 659, Loss: 0.288284
- Iter 524 / 659, Loss: 0.499467
- Iter 655 / 659, Loss: 0.242255

* Train accuracy / confusion: 86.16% / [[7509, 862, 173], [873, 6214, 403], [168, 440, 4446]], 
* Val   accuracy / confusion: 79.24% / [[902, 138, 29], [172, 682, 84], [37, 88, 508]]

------------------------------ Epoch 170 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.346654
- Iter 262 / 659, Loss: 0.454095
- Iter 393 / 659, Loss: 0.356610
- Iter 524 / 659, Loss: 0.265079
- Iter 655 / 659, Loss: 0.541825

* Train accuracy / confusion: 86.02% / [[7463, 873, 207], [884, 6216, 389], [167, 428, 4461]], 
* Val   accuracy / confusion: 79.36% / [[875, 167, 27], [141, 738, 59], [45, 106, 482]]

------------------------------ Epoch 171 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.358087
- Iter 262 / 659, Loss: 0.343926
- Iter 393 / 659, Loss: 0.346945
- Iter 524 / 659, Loss: 0.355963
- Iter 655 / 659, Loss: 0.429683

* Train accuracy / confusion: 86.38% / [[7481, 864, 197], [833, 6256, 401], [133, 445, 4478]], 
* Val   accuracy / confusion: 79.43% / [[894, 156, 19], [163, 718, 57], [53, 95, 485]]

------------------------------ Epoch 172 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.615223
- Iter 262 / 659, Loss: 0.216705
- Iter 393 / 659, Loss: 0.507440
- Iter 524 / 659, Loss: 0.351880
- Iter 655 / 659, Loss: 0.199820

* Train accuracy / confusion: 86.75% / [[7529, 809, 202], [847, 6264, 382], [163, 392, 4500]], 
* Val   accuracy / confusion: 80.38% / [[909, 133, 27], [159, 722, 57], [51, 91, 491]]

------------------------------ Epoch 173 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.201009
- Iter 262 / 659, Loss: 0.267302
- Iter 393 / 659, Loss: 0.242760
- Iter 524 / 659, Loss: 0.479058
- Iter 655 / 659, Loss: 0.304195

* Train accuracy / confusion: 86.30% / [[7453, 902, 189], [860, 6247, 381], [159, 398, 4499]], 
* Val   accuracy / confusion: 79.92% / [[890, 151, 28], [152, 716, 70], [37, 92, 504]]

------------------------------ Epoch 174 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.359379
- Iter 262 / 659, Loss: 0.278898
- Iter 393 / 659, Loss: 0.372863
- Iter 524 / 659, Loss: 0.341781
- Iter 655 / 659, Loss: 0.432150

* Train accuracy / confusion: 86.22% / [[7489, 869, 184], [854, 6227, 411], [168, 419, 4467]], 
* Val   accuracy / confusion: 79.70% / [[911, 137, 21], [178, 696, 64], [41, 95, 497]]

------------------------------ Epoch 175 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.209442
- Iter 262 / 659, Loss: 0.658766
- Iter 393 / 659, Loss: 0.289884
- Iter 524 / 659, Loss: 0.190069
- Iter 655 / 659, Loss: 0.369926

* Train accuracy / confusion: 86.75% / [[7504, 844, 197], [817, 6279, 397], [143, 396, 4511]], 
* Val   accuracy / confusion: 78.98% / [[864, 167, 38], [135, 725, 78], [40, 97, 496]]

------------------------------ Epoch 176 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.635788
- Iter 262 / 659, Loss: 0.119974
- Iter 393 / 659, Loss: 0.652029
- Iter 524 / 659, Loss: 0.392382
- Iter 655 / 659, Loss: 0.448246

* Train accuracy / confusion: 86.67% / [[7490, 864, 186], [853, 6277, 362], [144, 401, 4511]], 
* Val   accuracy / confusion: 80.53% / [[904, 144, 21], [155, 715, 68], [37, 89, 507]]

------------------------------ Epoch 177 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.309771
- Iter 262 / 659, Loss: 0.422341
- Iter 393 / 659, Loss: 0.408478
- Iter 524 / 659, Loss: 0.246742
- Iter 655 / 659, Loss: 0.392877

* Train accuracy / confusion: 86.03% / [[7466, 876, 203], [877, 6215, 400], [167, 423, 4461]], 
* Val   accuracy / confusion: 79.55% / [[907, 135, 27], [170, 707, 61], [52, 95, 486]]

------------------------------ Epoch 178 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.312811
- Iter 262 / 659, Loss: 0.345818
- Iter 393 / 659, Loss: 0.382368
- Iter 524 / 659, Loss: 0.304702
- Iter 655 / 659, Loss: 0.322549

* Train accuracy / confusion: 86.19% / [[7505, 846, 188], [906, 6204, 383], [153, 436, 4467]], 
* Val   accuracy / confusion: 79.55% / [[886, 160, 23], [132, 731, 75], [40, 110, 483]]

------------------------------ Epoch 179 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.446498
- Iter 262 / 659, Loss: 0.179219
- Iter 393 / 659, Loss: 0.229305
- Iter 524 / 659, Loss: 0.419991
- Iter 655 / 659, Loss: 0.368139

* Train accuracy / confusion: 86.51% / [[7501, 870, 173], [829, 6243, 416], [156, 401, 4499]], 
* Val   accuracy / confusion: 80.23% / [[888, 151, 30], [148, 727, 63], [39, 91, 503]]

------------------------------ Epoch 180 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.262776
- Iter 262 / 659, Loss: 0.309312
- Iter 393 / 659, Loss: 0.188882
- Iter 524 / 659, Loss: 0.393738
- Iter 655 / 659, Loss: 0.322689

* Train accuracy / confusion: 86.26% / [[7490, 857, 198], [885, 6208, 398], [161, 399, 4492]], 
* Val   accuracy / confusion: 78.94% / [[870, 154, 45], [143, 703, 92], [37, 85, 511]]

------------------------------ Epoch 181 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.421264
- Iter 262 / 659, Loss: 0.236226
- Iter 393 / 659, Loss: 0.200343
- Iter 524 / 659, Loss: 0.211674
- Iter 655 / 659, Loss: 0.330268

* Train accuracy / confusion: 86.51% / [[7509, 865, 167], [847, 6273, 370], [152, 443, 4462]], 
* Val   accuracy / confusion: 80.11% / [[886, 153, 30], [139, 728, 71], [36, 96, 501]]

------------------------------ Epoch 182 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.323233
- Iter 262 / 659, Loss: 0.260809
- Iter 393 / 659, Loss: 0.187150
- Iter 524 / 659, Loss: 0.186588
- Iter 655 / 659, Loss: 0.390585

* Train accuracy / confusion: 86.49% / [[7524, 851, 168], [892, 6221, 378], [146, 413, 4495]], 
* Val   accuracy / confusion: 80.72% / [[904, 140, 25], [147, 718, 73], [49, 75, 509]]

------------------------------ Epoch 183 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.424726
- Iter 262 / 659, Loss: 0.616939
- Iter 393 / 659, Loss: 0.416400
- Iter 524 / 659, Loss: 0.312261
- Iter 655 / 659, Loss: 0.166407

* Train accuracy / confusion: 86.54% / [[7530, 821, 192], [848, 6243, 403], [166, 409, 4476]], 
* Val   accuracy / confusion: 79.70% / [[869, 167, 33], [146, 728, 64], [34, 92, 507]]

------------------------------ Epoch 184 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.347562
- Iter 262 / 659, Loss: 0.225742
- Iter 393 / 659, Loss: 0.485732
- Iter 524 / 659, Loss: 0.250897
- Iter 655 / 659, Loss: 0.287945

* Train accuracy / confusion: 86.47% / [[7485, 869, 189], [855, 6267, 368], [167, 406, 4482]], 
* Val   accuracy / confusion: 79.13% / [[905, 131, 33], [172, 677, 89], [38, 88, 507]]

------------------------------ Epoch 185 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.312644
- Iter 262 / 659, Loss: 0.549256
- Iter 393 / 659, Loss: 0.427259
- Iter 524 / 659, Loss: 0.270428
- Iter 655 / 659, Loss: 0.566481

* Train accuracy / confusion: 86.32% / [[7506, 844, 195], [835, 6237, 418], [168, 424, 4461]], 
* Val   accuracy / confusion: 80.11% / [[899, 148, 22], [133, 734, 71], [49, 102, 482]]

------------------------------ Epoch 186 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.318188
- Iter 262 / 659, Loss: 0.628193
- Iter 393 / 659, Loss: 0.330376
- Iter 524 / 659, Loss: 0.304966
- Iter 655 / 659, Loss: 0.293863

* Train accuracy / confusion: 86.12% / [[7460, 880, 204], [886, 6209, 394], [175, 388, 4492]], 
* Val   accuracy / confusion: 80.30% / [[903, 137, 29], [154, 720, 64], [43, 93, 497]]

------------------------------ Epoch 187 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.244320
- Iter 262 / 659, Loss: 0.415900
- Iter 393 / 659, Loss: 0.562289
- Iter 524 / 659, Loss: 0.360680
- Iter 655 / 659, Loss: 0.467583

* Train accuracy / confusion: 86.46% / [[7548, 818, 177], [874, 6212, 405], [158, 424, 4472]], 
* Val   accuracy / confusion: 79.77% / [[879, 154, 36], [140, 729, 69], [30, 105, 498]]

------------------------------ Epoch 188 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.411683
- Iter 262 / 659, Loss: 0.200894
- Iter 393 / 659, Loss: 0.345008
- Iter 524 / 659, Loss: 0.211707
- Iter 655 / 659, Loss: 0.322426

* Train accuracy / confusion: 86.64% / [[7506, 859, 176], [833, 6275, 380], [174, 396, 4489]], 
* Val   accuracy / confusion: 79.73% / [[896, 148, 25], [160, 712, 66], [40, 96, 497]]

------------------------------ Epoch 189 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.414072
- Iter 262 / 659, Loss: 0.464950
- Iter 393 / 659, Loss: 0.267448
- Iter 524 / 659, Loss: 0.627869
- Iter 655 / 659, Loss: 0.308646

* Train accuracy / confusion: 86.12% / [[7465, 880, 199], [866, 6225, 397], [171, 415, 4470]], 
* Val   accuracy / confusion: 79.77% / [[881, 159, 29], [144, 722, 72], [38, 92, 503]]

------------------------------ Epoch 190 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.390198
- Iter 262 / 659, Loss: 0.297269
- Iter 393 / 659, Loss: 0.551158
- Iter 524 / 659, Loss: 0.396331
- Iter 655 / 659, Loss: 0.290557

* Train accuracy / confusion: 86.70% / [[7520, 858, 169], [824, 6267, 398], [152, 403, 4497]], 
* Val   accuracy / confusion: 78.60% / [[841, 199, 29], [128, 747, 63], [30, 116, 487]]

------------------------------ Epoch 191 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.429385
- Iter 262 / 659, Loss: 0.366767
- Iter 393 / 659, Loss: 0.161527
- Iter 524 / 659, Loss: 0.436057
- Iter 655 / 659, Loss: 0.212621

* Train accuracy / confusion: 86.59% / [[7532, 820, 192], [854, 6264, 372], [162, 428, 4464]], 
* Val   accuracy / confusion: 79.47% / [[893, 150, 26], [148, 729, 61], [37, 120, 476]]

------------------------------ Epoch 192 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.263108
- Iter 262 / 659, Loss: 0.353142
- Iter 393 / 659, Loss: 0.627724
- Iter 524 / 659, Loss: 0.285432
- Iter 655 / 659, Loss: 0.347363

* Train accuracy / confusion: 86.37% / [[7518, 841, 183], [870, 6212, 406], [166, 408, 4484]], 
* Val   accuracy / confusion: 79.05% / [[877, 159, 33], [148, 719, 71], [42, 100, 491]]

------------------------------ Epoch 193 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.281931
- Iter 262 / 659, Loss: 0.408093
- Iter 393 / 659, Loss: 0.438236
- Iter 524 / 659, Loss: 0.229119
- Iter 655 / 659, Loss: 0.387982

* Train accuracy / confusion: 86.52% / [[7497, 856, 191], [845, 6263, 379], [158, 413, 4486]], 
* Val   accuracy / confusion: 78.94% / [[888, 157, 24], [159, 694, 85], [37, 94, 502]]

------------------------------ Epoch 194 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.441684
- Iter 262 / 659, Loss: 0.352850
- Iter 393 / 659, Loss: 0.380533
- Iter 524 / 659, Loss: 0.421534
- Iter 655 / 659, Loss: 0.565569

* Train accuracy / confusion: 86.36% / [[7514, 831, 197], [837, 6238, 418], [165, 428, 4460]], 
* Val   accuracy / confusion: 78.86% / [[903, 132, 34], [171, 684, 83], [49, 89, 495]]

------------------------------ Epoch 195 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.225290
- Iter 262 / 659, Loss: 0.362095
- Iter 393 / 659, Loss: 0.236425
- Iter 524 / 659, Loss: 0.409041
- Iter 655 / 659, Loss: 0.121463

* Train accuracy / confusion: 86.26% / [[7504, 864, 176], [889, 6225, 371], [163, 435, 4461]], 
* Val   accuracy / confusion: 78.56% / [[866, 176, 27], [159, 707, 72], [40, 92, 501]]

------------------------------ Epoch 196 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.376408
- Iter 262 / 659, Loss: 0.309340
- Iter 393 / 659, Loss: 0.250525
- Iter 524 / 659, Loss: 0.439281
- Iter 655 / 659, Loss: 0.329959

* Train accuracy / confusion: 86.58% / [[7496, 853, 198], [865, 6238, 385], [143, 386, 4524]], 
* Val   accuracy / confusion: 80.30% / [[924, 119, 26], [183, 679, 76], [45, 71, 517]]

------------------------------ Epoch 197 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.510530
- Iter 262 / 659, Loss: 0.371816
- Iter 393 / 659, Loss: 0.487190
- Iter 524 / 659, Loss: 0.201849
- Iter 655 / 659, Loss: 0.461143

* Train accuracy / confusion: 86.17% / [[7484, 869, 189], [880, 6215, 397], [150, 431, 4473]], 
* Val   accuracy / confusion: 78.64% / [[922, 131, 16], [168, 719, 51], [68, 130, 435]]

------------------------------ Epoch 198 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.302985
- Iter 262 / 659, Loss: 0.390417
- Iter 393 / 659, Loss: 0.202686
- Iter 524 / 659, Loss: 0.255113
- Iter 655 / 659, Loss: 0.180352

* Train accuracy / confusion: 86.62% / [[7514, 840, 192], [840, 6269, 380], [153, 416, 4484]], 
* Val   accuracy / confusion: 80.19% / [[910, 138, 21], [155, 721, 62], [49, 98, 486]]

------------------------------ Epoch 199 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.271345
- Iter 262 / 659, Loss: 0.249228
- Iter 393 / 659, Loss: 0.418356
- Iter 524 / 659, Loss: 0.407946
- Iter 655 / 659, Loss: 0.428174

* Train accuracy / confusion: 86.67% / [[7543, 820, 183], [854, 6226, 412], [156, 385, 4509]], 
* Val   accuracy / confusion: 80.34% / [[892, 157, 20], [155, 736, 47], [44, 96, 493]]

------------------------------ Epoch 200 / 200, Learning rate: 1.16e-04 ------------------------------
- Iter 131 / 659, Loss: 0.435984
- Iter 262 / 659, Loss: 0.355632
- Iter 393 / 659, Loss: 0.307016
- Iter 524 / 659, Loss: 0.343985
- Iter 655 / 659, Loss: 0.140141

* Train accuracy / confusion: 86.98% / [[7574, 787, 180], [830, 6261, 403], [153, 392, 4508]], 
* Val   accuracy / confusion: 78.98% / [[854, 187, 28], [126, 732, 80], [29, 105, 499]]

**************************************** Training Ends ****************************************
- Test accuracy: 79.15%

- Confusion matrix:
 [[25815  5195  1060]
 [ 4232 21224  2654]
 [  858  2503 15599]]

print('- Debug table:')
pprint.pp(test_debug, indent=2, width=100)

- Debug table:
{ '14546': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00865039_170816'},
  '00719': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00164098_041214'},
  '30664': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [0, 24, 6], 'edfname': '01303196_020317'},
  '04086': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00498791_260916'},
  '05765': {'GT': 2, 'Acc': ' 56.67%', 'Pred': [0, 13, 17], 'edfname': '00623681_070415'},
  '19208': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01008809_021017'},
  '19516': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01019016_241115'},
  '36174': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01430323_060819'},
  '12726': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '00796686_020819'},
  '31736': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '01324136_201217'},
  '07979': {'GT': 1, 'Acc': ' 63.33%', 'Pred': [0, 19, 11], 'edfname': '00673505_020419'},
  '18204': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '00971724_030414'},
  '10785': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 0, 2], 'edfname': '00740750_110315'},
  '17786': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00958455_200917'},
  '06324': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00634467_180717'},
  '12220': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [0, 28, 2], 'edfname': '00776486_121016'},
  '13629': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00824216_080416'},
  '04527': {'GT': 2, 'Acc': ' 23.33%', 'Pred': [5, 18, 7], 'edfname': '00601765_231118'},
  '23994': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01154834_180417'},
  '03080': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00406530_310719'},
  '30733': {'GT': 2, 'Acc': ' 76.67%', 'Pred': [1, 6, 23], 'edfname': '01303263_281116'},
  '23424': {'GT': 0, 'Acc': ' 46.67%', 'Pred': [14, 16, 0], 'edfname': '01147097_121119'},
  '24505': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [0, 24, 6], 'edfname': '01164481_220615'},
  '18902': {'GT': 0, 'Acc': ' 43.33%', 'Pred': [13, 17, 0], 'edfname': '00988618_270618'},
  '32089': {'GT': 1, 'Acc': ' 36.67%', 'Pred': [4, 11, 15], 'edfname': '01334787_211117'},
  '26285': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01212635_270515'},
  '16435': {'GT': 2, 'Acc': '  0.00%', 'Pred': [29, 1, 0], 'edfname': '00926040_061115'},
  '19711': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01026841_270116'},
  '02693': {'GT': 0, 'Acc': ' 50.00%', 'Pred': [15, 8, 7], 'edfname': '00388436_200616'},
  '06604': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 5, 0], 'edfname': '00642740_120918'},
  '28610': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01266829_070416'},
  '17149': {'GT': 2, 'Acc': ' 76.67%', 'Pred': [0, 7, 23], 'edfname': '00951066_131217'},
  '25105': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [2, 26, 2], 'edfname': '01186214'},
  '03473': {'GT': 2, 'Acc': ' 43.33%', 'Pred': [8, 9, 13], 'edfname': '00430821_100217'},
  '30539': {'GT': 0, 'Acc': ' 16.67%', 'Pred': [5, 25, 0], 'edfname': '01300391_251116'},
  '27284': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [1, 28, 1], 'edfname': '01240949_191015'},
  '31301': {'GT': 2, 'Acc': ' 23.33%', 'Pred': [10, 13, 7], 'edfname': '01312293_120417'},
  '25411': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [0, 2, 28], 'edfname': '01195277_151119'},
  '16248': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00916546_281114'},
  '23430': {'GT': 0, 'Acc': ' 20.00%', 'Pred': [6, 24, 0], 'edfname': '01147097_121119'},
  '07382': {'GT': 1, 'Acc': ' 53.33%', 'Pred': [5, 16, 9], 'edfname': '00659706_220218'},
  '19978': {'GT': 0, 'Acc': ' 20.00%', 'Pred': [6, 24, 0], 'edfname': '01035046_020715'},
  '32422': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01338710_261017'},
  '15256': {'GT': 1, 'Acc': ' 26.67%', 'Pred': [14, 8, 8], 'edfname': '00888613_060819'},
  '23265': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01145402_161117'},
  '18116': {'GT': 1, 'Acc': ' 40.00%', 'Pred': [4, 12, 14], 'edfname': '00968332_310518'},
  '14219': {'GT': 1, 'Acc': ' 63.33%', 'Pred': [10, 19, 1], 'edfname': '00851302_261217'},
  '10932': {'GT': 2, 'Acc': ' 90.00%', 'Pred': [2, 1, 27], 'edfname': '00743464_220316'},
  '27547': {'GT': 1, 'Acc': ' 50.00%', 'Pred': [15, 15, 0], 'edfname': '01242982_110416'},
  '27439': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [0, 25, 5], 'edfname': '01242449_230617'},
  '26232': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01211503_070415'},
  '09156': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00699594_011217'},
  '23730': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [3, 27, 0], 'edfname': '01150270_020617'},
  '06750': {'GT': 2, 'Acc': ' 80.00%', 'Pred': [2, 4, 24], 'edfname': '00646518_240217'},
  '25601': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01200378_230117'},
  '10096': {'GT': 0, 'Acc': ' 76.67%', 'Pred': [23, 7, 0], 'edfname': '00723144_180118'},
  '19457': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01018538_070514'},
  '28228': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01260350_090316'},
  '31069': {'GT': 1, 'Acc': ' 76.67%', 'Pred': [0, 23, 7], 'edfname': '01307883_100217'},
  '28183': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 4, 0], 'edfname': '01256391_310316'},
  '03547': {'GT': 1, 'Acc': ' 40.00%', 'Pred': [0, 12, 18], 'edfname': '00433859_230519'},
  '26203': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01211467_070415'},
  '20941': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01086373_290415'},
  '18188': {'GT': 0, 'Acc': ' 43.33%', 'Pred': [13, 8, 9], 'edfname': '00971724_030414'},
  '17148': {'GT': 2, 'Acc': ' 70.00%', 'Pred': [1, 8, 21], 'edfname': '00951066_131217'},
  '14254': {'GT': 1, 'Acc': '  3.33%', 'Pred': [29, 1, 0], 'edfname': '00851511_200818'},
  '19266': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01010278_281116'},
  '13049': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [1, 26, 3], 'edfname': '00805584_060717'},
  '00610': {'GT': 0, 'Acc': ' 63.33%', 'Pred': [19, 6, 5], 'edfname': '00160843_080219'},
  '22878': {'GT': 0, 'Acc': ' 10.00%', 'Pred': [3, 27, 0], 'edfname': '01139946_120214'},
  '28828': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [0, 28, 2], 'edfname': '01272370_271217'},
  '12287': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '00777684_300119'},
  '08086': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00675516_021019'},
  '03376': {'GT': 0, 'Acc': ' 26.67%', 'Pred': [8, 21, 1], 'edfname': '00422970_151118'},
  '02676': {'GT': 0, 'Acc': ' 76.67%', 'Pred': [23, 7, 0], 'edfname': '00385518_130815'},
  '06492': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00640147_140519'},
  '22749': {'GT': 0, 'Acc': ' 13.33%', 'Pred': [4, 26, 0], 'edfname': '01139939_110214'},
  '25085': {'GT': 0, 'Acc': '  6.67%', 'Pred': [2, 0, 28], 'edfname': '01183615_201014'},
  '05069': {'GT': 2, 'Acc': ' 33.33%', 'Pred': [6, 14, 10], 'edfname': '00609255_140417'},
  '25154': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 1, 1], 'edfname': '01186934_200519'},
  '07529': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00664961_261218'},
  '27329': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01241945_191115'},
  '27878': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '01251650_090819'},
  '11091': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00747390_130918'},
  '16040': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00907205_241019'},
  '07121': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '00654091_080618'},
  '26019': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01208998_240315'},
  '16147': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00912302_100415'},
  '34624': {'GT': 2, 'Acc': ' 86.67%', 'Pred': [0, 4, 26], 'edfname': '01392274_020320'},
  '28960': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01274934'},
  '27971': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01252396_141216'},
  '15958': {'GT': 2, 'Acc': ' 83.33%', 'Pred': [0, 5, 25], 'edfname': '00906868_151214'},
  '07550': {'GT': 2, 'Acc': ' 53.33%', 'Pred': [5, 9, 16], 'edfname': '00665084_280219'},
  '08793': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00690996_100817'},
  '36297': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01437071_200519'},
  '36250': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01432257_150419'},
  '29833': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01289759_110517'},
  '18141': {'GT': 1, 'Acc': ' 50.00%', 'Pred': [13, 15, 2], 'edfname': '00970997_301018'},
  '14175': {'GT': 1, 'Acc': ' 60.00%', 'Pred': [12, 18, 0], 'edfname': '00850889_290817'},
  '26597': {'GT': 0, 'Acc': ' 70.00%', 'Pred': [21, 9, 0], 'edfname': '01222061_271017'},
  '20972': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01090425_290419'},
  '17036': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [0, 2, 28], 'edfname': '00949701_180219'},
  '17398': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [0, 24, 6], 'edfname': '00954188_160118'},
  '01734': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00290547_051018'},
  '20456': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01063007_221215'},
  '03675': {'GT': 0, 'Acc': ' 20.00%', 'Pred': [6, 24, 0], 'edfname': '00458897_141016'},
  '27406': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01241989_101115'},
  '24727': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '01174162_090817'},
  '24504': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '01164481_220615'},
  '23455': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '01147098_060515'},
  '12258': {'GT': 2, 'Acc': ' 20.00%', 'Pred': [0, 24, 6], 'edfname': '00777577_120314'},
  '02173': {'GT': 0, 'Acc': ' 16.67%', 'Pred': [5, 25, 0], 'edfname': '00326875_050314'},
  '27217': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01238327_050819'},
  '15889': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00906868_040116'},
  '30651': {'GT': 0, 'Acc': '  6.67%', 'Pred': [2, 28, 0], 'edfname': '01302961_060117'},
  '00107': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00048377_070819'},
  '27790': {'GT': 2, 'Acc': ' 86.67%', 'Pred': [0, 4, 26], 'edfname': '01250004_260116'},
  '01378': {'GT': 0, 'Acc': ' 70.00%', 'Pred': [21, 5, 4], 'edfname': '00250122_100519'},
  '21850': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01131959_310118'},
  '22936': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01139949_060214'},
  '06400': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [0, 25, 5], 'edfname': '00635487_161019'},
  '15666': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00899380_060515'},
  '00143': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00048377_250719'},
  '19446': {'GT': 0, 'Acc': ' 80.00%', 'Pred': [24, 6, 0], 'edfname': '01017337_140219'},
  '24317': {'GT': 0, 'Acc': ' 50.00%', 'Pred': [15, 15, 0], 'edfname': '01159256_220514'},
  '14965': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [0, 24, 6], 'edfname': '00883719_030912'},
  '06259': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00634366_241019'},
  '27865': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '01251650_090819'},
  '15759': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [3, 26, 1], 'edfname': '00899964_060115'},
  '15213': {'GT': 0, 'Acc': '  0.00%', 'Pred': [0, 25, 5], 'edfname': '00887981_210617'},
  '25614': {'GT': 1, 'Acc': ' 73.33%', 'Pred': [0, 22, 8], 'edfname': '01200378_230117'},
  '01803': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00293228_120718'},
  '22688': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01139924_291214'},
  '20240': {'GT': 1, 'Acc': ' 56.67%', 'Pred': [13, 17, 0], 'edfname': '01044712_261016'},
  '11672': {'GT': 2, 'Acc': ' 56.67%', 'Pred': [3, 10, 17], 'edfname': '00762439_081217'},
  '25547': {'GT': 2, 'Acc': ' 46.67%', 'Pred': [1, 15, 14], 'edfname': '01200248_100215'},
  '17284': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00952170_060516'},
  '03752': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '00474121_161118'},
  '29471': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01282315_040716'},
  '12112': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [4, 26, 0], 'edfname': '00773108_250615'},
  '13943': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00840844_071118'},
  '19717': {'GT': 2, 'Acc': ' 80.00%', 'Pred': [5, 1, 24], 'edfname': '01026990_241215'},
  '21313': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [1, 28, 1], 'edfname': '01112291_231115'},
  '02675': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 4, 0], 'edfname': '00385518_130815'},
  '33692': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01364379_260919'},
  '03441': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 3, 1], 'edfname': '00429185_130917'},
  '00159': {'GT': 2, 'Acc': ' 86.67%', 'Pred': [0, 4, 26], 'edfname': '00048377_250719'},
  '35064': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [2, 27, 1], 'edfname': '01402378_110219'},
  '11354': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00753842_110517'},
  '02990': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '00401196_061218'},
  '16927': {'GT': 1, 'Acc': ' 50.00%', 'Pred': [12, 15, 3], 'edfname': '00945348_170215'},
  '17054': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00950450_170615'},
  '15439': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [1, 28, 1], 'edfname': '00891889_060717'},
  '13871': {'GT': 2, 'Acc': ' 60.00%', 'Pred': [0, 12, 18], 'edfname': '00840062_080519'},
  '15796': {'GT': 1, 'Acc': ' 30.00%', 'Pred': [21, 9, 0], 'edfname': '00899964_110414'},
  '00523': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00138259_030518'},
  '18294': {'GT': 0, 'Acc': ' 23.33%', 'Pred': [7, 23, 0], 'edfname': '00978066_170815'},
  '04159': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00502785_121219'},
  '08088': {'GT': 2, 'Acc': ' 90.00%', 'Pred': [0, 3, 27], 'edfname': '00675516_021019'},
  '36451': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01468518_120320'},
  '16062': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00907205_241019'},
  '17787': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00958455_200917'},
  '34598': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [2, 26, 2], 'edfname': '01392155_110918'},
  '22250': {'GT': 1, 'Acc': '  0.00%', 'Pred': [30, 0, 0], 'edfname': '01135545_230715'},
  '04299': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00518633_031215'},
  '22144': {'GT': 0, 'Acc': ' 40.00%', 'Pred': [12, 18, 0], 'edfname': '01134557_301116'},
  '09007': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00695272_100519'},
  '10170': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00725683_091115'},
  '08376': {'GT': 1, 'Acc': ' 50.00%', 'Pred': [14, 15, 1], 'edfname': '00678916_020718'},
  '11766': {'GT': 2, 'Acc': ' 60.00%', 'Pred': [2, 10, 18], 'edfname': '00763580_281118'},
  '16267': {'GT': 1, 'Acc': ' 26.67%', 'Pred': [0, 8, 22], 'edfname': '00916613_240813'},
  '07307': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00658017_180917'},
  '02626': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00378481_210617'},
  '29353': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01280027_240616'},
  '30266': {'GT': 1, 'Acc': '  6.67%', 'Pred': [0, 2, 28], 'edfname': '01296557_160120'},
  '30077': {'GT': 0, 'Acc': ' 63.33%', 'Pred': [19, 11, 0], 'edfname': '01296102_150217'},
  '20013': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '01035162_180119'},
  '10918': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 4, 1], 'edfname': '00743001_250618'},
  '24212': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01156406_120319'},
  '30253': {'GT': 1, 'Acc': ' 20.00%', 'Pred': [0, 6, 24], 'edfname': '01296557_160120'},
  '08502': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00684221_101016'},
  '02068': {'GT': 1, 'Acc': '  0.00%', 'Pred': [29, 0, 1], 'edfname': '00317645_311016'},
  '20664': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01071934_070515'},
  '25653': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 3, 2], 'edfname': '01200993_060315'},
  '14627': {'GT': 1, 'Acc': ' 13.33%', 'Pred': [26, 4, 0], 'edfname': '00869351_160217'},
  '34948': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01400560_160419'},
  '03920': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00484934_090514'},
  '00492': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00133390_220414'},
  '02979': {'GT': 0, 'Acc': ' 26.67%', 'Pred': [8, 5, 17], 'edfname': '00400036_120314'},
  '33707': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01364379_260919'},
  '10472': {'GT': 2, 'Acc': ' 80.00%', 'Pred': [6, 0, 24], 'edfname': '00732597_041217'},
  '21875': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01132052_230518'},
  '26978': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01235034_290120'},
  '18588': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [0, 2, 28], 'edfname': '00985827_060718'},
  '03900': {'GT': 1, 'Acc': ' 56.67%', 'Pred': [0, 17, 13], 'edfname': '00484899_260115'},
  '29930': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '01293646_120719'},
  '15255': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00888613_060819'},
  '00774': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [0, 27, 3], 'edfname': '00164098_180919'},
  '23320': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01145493_030414'},
  '31053': {'GT': 1, 'Acc': ' 30.00%', 'Pred': [21, 9, 0], 'edfname': '01307080_060117'},
  '26378': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01215547_290415'},
  '15237': {'GT': 0, 'Acc': '  0.00%', 'Pred': [0, 29, 1], 'edfname': '00887981_210617'},
  '09896': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '00720119_200318'},
  '34875': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [5, 25, 0], 'edfname': '01397646_281019'},
  '30036': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [0, 28, 2], 'edfname': '01295899_041016'},
  '22772': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01139940_290114'},
  '32061': {'GT': 2, 'Acc': ' 60.00%', 'Pred': [5, 7, 18], 'edfname': '01334756_191217'},
  '28568': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '01266696_300316'},
  '12454': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00783696_220414'},
  '34153': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01376302_060718'},
  '11154': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 0, 2], 'edfname': '00751493_210514'},
  '02005': {'GT': 0, 'Acc': ' 40.00%', 'Pred': [12, 7, 11], 'edfname': '00316358_050815'},
  '11345': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [0, 27, 3], 'edfname': '00753842_110517'},
  '26348': {'GT': 1, 'Acc': ' 10.00%', 'Pred': [27, 3, 0], 'edfname': '01215115_270715'},
  '19719': {'GT': 2, 'Acc': ' 86.67%', 'Pred': [3, 1, 26], 'edfname': '01026990_241215'},
  '01305': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '00244987_050315'},
  '13611': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00823206_130514'},
  '27054': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01235272_170915'},
  '21667': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01128393_300715'},
  '13355': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00816768_260618'},
  '08441': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00682484_240718'},
  '29555': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01285216_140717'},
  '24417': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01161826_050916'},
  '01041': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00223407_161219'},
  '24082': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01155730_070514'},
  '32141': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01335435_280617'},
  '28607': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01266829_070416'},
  '05146': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [1, 28, 1], 'edfname': '00612060_221118'},
  '01945': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00313383_180314'},
  '28422': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01264839_070416'},
  '05723': {'GT': 1, 'Acc': ' 16.67%', 'Pred': [20, 5, 5], 'edfname': '00621729_020616'},
  '22569': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 3, 2], 'edfname': '01138910_270114'},
  '09438': {'GT': 0, 'Acc': ' 36.67%', 'Pred': [11, 18, 1], 'edfname': '00708122_201218'},
  '07438': {'GT': 2, 'Acc': ' 20.00%', 'Pred': [0, 24, 6], 'edfname': '00661209_010816'},
  '28067': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '01253550_240519'},
  '20516': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01064740_231214'},
  '31289': {'GT': 2, 'Acc': ' 40.00%', 'Pred': [0, 18, 12], 'edfname': '01312293_120417'},
  '01674': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00287432_091219'},
  '16033': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00906868_270616'},
  '23563': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01147101_190314'},
  '10582': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '00736916_310316'},
  '19988': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 0, 1], 'edfname': '01035046_020715'},
  '16029': {'GT': 2, 'Acc': ' 56.67%', 'Pred': [2, 11, 17], 'edfname': '00906868_270616'},
  '23515': {'GT': 0, 'Acc': ' 23.33%', 'Pred': [7, 20, 3], 'edfname': '01147099_130314'},
  '33519': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '01358607_280918'},
  '30691': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '01303198_020317'},
  '27771': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01249485_110316'},
  '07255': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00657017_281218'},
  '29532': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01284214_160816'},
  '05049': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00609184_070618'},
  '19675': {'GT': 0, 'Acc': ' 50.00%', 'Pred': [15, 15, 0], 'edfname': '01026841_101215'},
  '27508': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 1, 1], 'edfname': '01242663_271015'},
  '25430': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [0, 2, 28], 'edfname': '01195277_291215'},
  '25887': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '01203444_240719'},
  '18152': {'GT': 1, 'Acc': ' 76.67%', 'Pred': [6, 23, 1], 'edfname': '00970997_301018'},
  '06401': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00635487_161019'},
  '31846': {'GT': 2, 'Acc': ' 56.67%', 'Pred': [1, 12, 17], 'edfname': '01329469_120118'},
  '25871': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [5, 25, 0], 'edfname': '01203444_240719'},
  '30602': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01301982_230118'},
  '08225': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00675516_270214'},
  '16578': {'GT': 1, 'Acc': ' 46.67%', 'Pred': [16, 14, 0], 'edfname': '00931073_181018'},
  '08355': {'GT': 1, 'Acc': '  0.00%', 'Pred': [0, 0, 30], 'edfname': '00678829_160217'},
  '20200': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01043631_211117'},
  '30549': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01300391_251116'},
  '01663': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00287432_091219'},
  '19635': {'GT': 0, 'Acc': ' 40.00%', 'Pred': [12, 8, 10], 'edfname': '01025734_280715'},
  '03277': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 4, 1], 'edfname': '00417769_100117'},
  '34499': {'GT': 1, 'Acc': ' 70.00%', 'Pred': [8, 21, 1], 'edfname': '01382733_150518'},
  '06655': {'GT': 0, 'Acc': '  0.00%', 'Pred': [0, 30, 0], 'edfname': '00643352_261218'},
  '34374': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01381606_160518'},
  '12462': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '00784417_100315'},
  '15417': {'GT': 1, 'Acc': ' 20.00%', 'Pred': [9, 6, 15], 'edfname': '00891889_060717'},
  '14814': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00875350_140514'},
  '03506': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00430974_050315'},
  '22769': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '01139940_290114'},
  '32121': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01335435_121119'},
  '15571': {'GT': 1, 'Acc': ' 73.33%', 'Pred': [0, 22, 8], 'edfname': '00896042_310818'},
  '18956': {'GT': 1, 'Acc': '  0.00%', 'Pred': [0, 0, 30], 'edfname': '00996941_180517'},
  '18512': {'GT': 1, 'Acc': ' 43.33%', 'Pred': [10, 13, 7], 'edfname': '00984999_021117'},
  '28412': {'GT': 0, 'Acc': ' 80.00%', 'Pred': [24, 6, 0], 'edfname': '01264839_070416'},
  '13995': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00840844_250119'},
  '22834': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01139944_040214'},
  '18029': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00963680_280519'},
  '09961': {'GT': 2, 'Acc': ' 66.67%', 'Pred': [0, 10, 20], 'edfname': '00721815_280519'},
  '05132': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '00611999_041018'},
  '30647': {'GT': 0, 'Acc': '  6.67%', 'Pred': [2, 27, 1], 'edfname': '01302961_060117'},
  '35126': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01403371_201218'},
  '12264': {'GT': 1, 'Acc': ' 60.00%', 'Pred': [12, 18, 0], 'edfname': '00777684_300119'},
  '13340': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00816641_270214'},
  '27007': {'GT': 0, 'Acc': ' 16.67%', 'Pred': [5, 14, 11], 'edfname': '01235271_170915'},
  '17737': {'GT': 1, 'Acc': ' 20.00%', 'Pred': [24, 6, 0], 'edfname': '00957862_230317'},
  '22663': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01139924_140717'},
  '11813': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00764690_050319'},
  '29682': {'GT': 1, 'Acc': ' 30.00%', 'Pred': [1, 9, 20], 'edfname': '01286604_220218'},
  '35042': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 5, 0], 'edfname': '01402179_311018'},
  '34036': {'GT': 2, 'Acc': ' 53.33%', 'Pred': [6, 8, 16], 'edfname': '01370156_200218'},
  '16356': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00919146_120517'},
  '01529': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00270630_050314'},
  '32469': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '01339759_071119'},
  '17702': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00957827_230317'},
  '34865': {'GT': 1, 'Acc': ' 73.33%', 'Pred': [8, 22, 0], 'edfname': '01397646_281019'},
  '24007': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01154834_180417'},
  '28163': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01256391_240216'},
  '11809': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00764690_050319'},
  '02661': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00385518_010715'},
  '34329': {'GT': 2, 'Acc': ' 40.00%', 'Pred': [0, 18, 12], 'edfname': '01379199_220618'},
  '18262': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00977640_070918'},
  '25200': {'GT': 0, 'Acc': ' 53.33%', 'Pred': [16, 14, 0], 'edfname': '01189271_031215'},
  '17845': {'GT': 2, 'Acc': ' 66.67%', 'Pred': [2, 8, 20], 'edfname': '00960161_201014'},
  '08596': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '00685191_100419'},
  '06092': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00631178_110517'},
  '02982': {'GT': 1, 'Acc': ' 63.33%', 'Pred': [0, 19, 11], 'edfname': '00401196_061218'},
  '25818': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01203444_090819'},
  '32480': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01339759_071119'},
  '15263': {'GT': 1, 'Acc': ' 40.00%', 'Pred': [18, 12, 0], 'edfname': '00888613_060819'},
  '06413': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '00635487_301019'},
  '17248': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '00951861_241117'},
  '12383': {'GT': 1, 'Acc': '  0.00%', 'Pred': [0, 0, 30], 'edfname': '00781425_130718'},
  '32402': {'GT': 1, 'Acc': ' 40.00%', 'Pred': [0, 12, 18], 'edfname': '01338642_281019'},
  '12247': {'GT': 2, 'Acc': ' 56.67%', 'Pred': [9, 4, 17], 'edfname': '00777577_120314'},
  '27357': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 0, 4], 'edfname': '01241947_261115'},
  '06246': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [0, 28, 2], 'edfname': '00634366_150316'},
  '16752': {'GT': 2, 'Acc': ' 20.00%', 'Pred': [4, 20, 6], 'edfname': '00939852_140214'},
  '03087': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00406530_310719'},
  '19808': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01027623_060317'},
  '20131': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01041208_020818'},
  '19603': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 0, 1], 'edfname': '01025734_250815'},
  '26723': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01225382_080715'},
  '26811': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 4, 0], 'edfname': '01227694_070815'},
  '08101': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00675516_021019'},
  '25986': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '01207872_281118'},
  '18682': {'GT': 2, 'Acc': ' 90.00%', 'Pred': [1, 2, 27], 'edfname': '00986061_240215'},
  '30257': {'GT': 1, 'Acc': ' 53.33%', 'Pred': [0, 16, 14], 'edfname': '01296557_160120'},
  '07932': {'GT': 1, 'Acc': ' 23.33%', 'Pred': [23, 7, 0], 'edfname': '00672867_031116'},
  '36209': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01432133_160519'},
  '24268': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01156962_140514'},
  '02006': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00316358_050815'},
  '22770': {'GT': 0, 'Acc': ' 80.00%', 'Pred': [24, 6, 0], 'edfname': '01139940_290114'},
  '12272': {'GT': 1, 'Acc': ' 63.33%', 'Pred': [8, 19, 3], 'edfname': '00777684_300119'},
  '02660': {'GT': 0, 'Acc': ' 63.33%', 'Pred': [19, 3, 8], 'edfname': '00385518_010715'},
  '29101': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01276868_020616'},
  '23877': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 5, 0], 'edfname': '01152747_230414'},
  '10026': {'GT': 1, 'Acc': ' 76.67%', 'Pred': [0, 23, 7], 'edfname': '00722305_261018'},
  '23191': {'GT': 0, 'Acc': ' 70.00%', 'Pred': [21, 9, 0], 'edfname': '01142809_180214'},
  '27345': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01241945_231215'},
  '15098': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [6, 24, 0], 'edfname': '00885992_011216'},
  '01440': {'GT': 0, 'Acc': ' 36.67%', 'Pred': [11, 17, 2], 'edfname': '00258008_230617'},
  '34762': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '01393410_160818'},
  '04825': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00606315_300715'},
  '02854': {'GT': 2, 'Acc': ' 76.67%', 'Pred': [0, 7, 23], 'edfname': '00397252_011018'},
  '06396': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '00635487_161019'},
  '09218': {'GT': 1, 'Acc': ' 56.67%', 'Pred': [2, 17, 11], 'edfname': '00699668_310818'},
  '10838': {'GT': 1, 'Acc': ' 53.33%', 'Pred': [14, 16, 0], 'edfname': '00741780_281116'},
  '13134': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [1, 28, 1], 'edfname': '00809059_030715'},
  '06214': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00633563_061015'},
  '17224': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [3, 25, 2], 'edfname': '00951808_251116'},
  '15623': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00898306_010618'},
  '01675': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00287432_091219'},
  '24103': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01155731_070514'},
  '25300': {'GT': 0, 'Acc': ' 23.33%', 'Pred': [7, 23, 0], 'edfname': '01191562_091214'},
  '14918': {'GT': 2, 'Acc': ' 70.00%', 'Pred': [5, 4, 21], 'edfname': '00879020_080817'},
  '04190': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00502785_140316'},
  '24251': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01156961_140514'},
  '11689': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [1, 0, 29], 'edfname': '00762439_081217'},
  '10984': {'GT': 0, 'Acc': ' 50.00%', 'Pred': [15, 15, 0], 'edfname': '00744618_290319'},
  '24482': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '01163965_130718'},
  '04002': {'GT': 0, 'Acc': ' 43.33%', 'Pred': [13, 10, 7], 'edfname': '00489204_290116'},
  '22513': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '01138301_230114'},
  '11787': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00764690_050319'},
  '04278': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00507286_270715'},
  '25775': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01201484_150618'},
  '34183': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01376302_151019'},
  '32358': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [0, 28, 2], 'edfname': '01338642_081119'},
  '22679': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01139924_291214'},
  '13325': {'GT': 0, 'Acc': '  6.67%', 'Pred': [2, 27, 1], 'edfname': '00813343_041218'},
  '16340': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00919146_120517'},
  '25228': {'GT': 0, 'Acc': ' 56.67%', 'Pred': [17, 0, 13], 'edfname': '01190283_261114'},
  '31094': {'GT': 1, 'Acc': ' 56.67%', 'Pred': [1, 17, 12], 'edfname': '01307883_100217'},
  '29072': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01276737_300516'},
  '07372': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [3, 27, 0], 'edfname': '00659706_220218'},
  '08747': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00688242_040919'},
  '18669': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [0, 2, 28], 'edfname': '00986061_240215'},
  '30579': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01301982_151116'},
  '05454': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [0, 24, 6], 'edfname': '00617308_021216'},
  '01593': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [2, 27, 1], 'edfname': '00286931_201217'},
  '27782': {'GT': 1, 'Acc': ' 33.33%', 'Pred': [0, 10, 20], 'edfname': '01249485_110316'},
  '12407': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00783510_281015'},
  '10888': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 4, 0], 'edfname': '00742152_051016'},
  '29105': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01276868_020616'},
  '03254': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00416942_190516'},
  '04857': {'GT': 0, 'Acc': ' 76.67%', 'Pred': [23, 7, 0], 'edfname': '00606999_180817'},
  '35051': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01402378_110219'},
  '07107': {'GT': 1, 'Acc': ' 60.00%', 'Pred': [0, 18, 12], 'edfname': '00651389_281016'},
  '28849': {'GT': 1, 'Acc': ' 43.33%', 'Pred': [16, 13, 1], 'edfname': '01272370_271217'},
  '09431': {'GT': 0, 'Acc': ' 50.00%', 'Pred': [15, 15, 0], 'edfname': '00708122_201218'},
  '36324': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [1, 1, 28], 'edfname': '01446092_160819'},
  '30629': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 5, 0], 'edfname': '01302961_060117'},
  '12781': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [0, 27, 3], 'edfname': '00796686_231219'},
  '33877': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [1, 1, 28], 'edfname': '01367495_310118'},
  '30332': {'GT': 1, 'Acc': ' 60.00%', 'Pred': [12, 18, 0], 'edfname': '01298112_271016'},
  '30890': {'GT': 1, 'Acc': ' 73.33%', 'Pred': [0, 22, 8], 'edfname': '01306354_051216'},
  '28799': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01271298_270319'},
  '17597': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '00956048_010715'},
  '20489': {'GT': 2, 'Acc': ' 73.33%', 'Pred': [2, 6, 22], 'edfname': '01064650_161117'},
  '27554': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [5, 25, 0], 'edfname': '01242982_110416'},
  '18233': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 0, 4], 'edfname': '00977111_211218'},
  '34169': {'GT': 0, 'Acc': ' 20.00%', 'Pred': [6, 23, 1], 'edfname': '01376302_060718'},
  '20518': {'GT': 2, 'Acc': ' 70.00%', 'Pred': [9, 0, 21], 'edfname': '01064740_231214'},
  '33698': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01364379_260919'},
  '25308': {'GT': 0, 'Acc': ' 53.33%', 'Pred': [16, 14, 0], 'edfname': '01191562_091214'},
  '21711': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 0, 1], 'edfname': '01129008_170818'},
  '21275': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [0, 2, 28], 'edfname': '01104301_011214'},
  '36360': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01450766_200919'},
  '12816': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '00796775_281015'},
  '12807': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00796775_281015'},
  '29916': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [6, 24, 0], 'edfname': '01293646_120719'},
  '01114': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '00232310_261218'},
  '23972': {'GT': 0, 'Acc': ' 30.00%', 'Pred': [9, 21, 0], 'edfname': '01154834_011019'},
  '21552': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01126091_140415'},
  '24308': {'GT': 0, 'Acc': ' 53.33%', 'Pred': [16, 14, 0], 'edfname': '01159187_131118'},
  '35024': {'GT': 0, 'Acc': ' 53.33%', 'Pred': [16, 14, 0], 'edfname': '01402179_311018'},
  '07058': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00650788_280415'},
  '00442': {'GT': 1, 'Acc': ' 53.33%', 'Pred': [14, 16, 0], 'edfname': '00127458_150618'},
  '09921': {'GT': 0, 'Acc': ' 80.00%', 'Pred': [24, 6, 0], 'edfname': '00720119_200318'},
  '07289': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00657399_070515'},
  '27877': {'GT': 1, 'Acc': ' 76.67%', 'Pred': [3, 23, 4], 'edfname': '01251650_090819'},
  '33562': {'GT': 1, 'Acc': ' 10.00%', 'Pred': [0, 3, 27], 'edfname': '01361792_301018'},
  '08587': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [5, 25, 0], 'edfname': '00685191_100419'},
  '11038': {'GT': 2, 'Acc': ' 53.33%', 'Pred': [0, 14, 16], 'edfname': '00745209_220916'},
  '14995': {'GT': 1, 'Acc': ' 76.67%', 'Pred': [4, 23, 3], 'edfname': '00883719_240812'},
  '31058': {'GT': 1, 'Acc': ' 20.00%', 'Pred': [24, 6, 0], 'edfname': '01307080_060117'},
  '22890': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 5, 0], 'edfname': '01139946_120214'},
  '04913': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [0, 27, 3], 'edfname': '00607068_260617'},
  '27462': {'GT': 0, 'Acc': ' 70.00%', 'Pred': [21, 0, 9], 'edfname': '01242661_011215'},
  '03867': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00480292_231215'},
  '22931': {'GT': 0, 'Acc': ' 80.00%', 'Pred': [24, 6, 0], 'edfname': '01139949_060214'},
  '12863': {'GT': 2, 'Acc': ' 90.00%', 'Pred': [0, 3, 27], 'edfname': '00799528_270916'},
  '14529': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [0, 2, 28], 'edfname': '00862802_260618'},
  '07643': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00666711_140218'},
  '17846': {'GT': 2, 'Acc': ' 16.67%', 'Pred': [0, 25, 5], 'edfname': '00960161_201014'},
  '27400': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '01241989_101115'},
  '32363': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '01338642_081119'},
  '17859': {'GT': 0, 'Acc': ' 53.33%', 'Pred': [16, 14, 0], 'edfname': '00961326_010817'},
  '31027': {'GT': 0, 'Acc': ' 80.00%', 'Pred': [24, 6, 0], 'edfname': '01306737_151216'},
  '04543': {'GT': 2, 'Acc': ' 33.33%', 'Pred': [0, 20, 10], 'edfname': '00601765_231118'},
  '06608': {'GT': 0, 'Acc': ' 56.67%', 'Pred': [17, 13, 0], 'edfname': '00642740_120918'},
  '21611': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 4, 0], 'edfname': '01127053_290818'},
  '06922': {'GT': 2, 'Acc': ' 86.67%', 'Pred': [0, 4, 26], 'edfname': '00649687_041019'},
  '16470': {'GT': 2, 'Acc': ' 90.00%', 'Pred': [0, 3, 27], 'edfname': '00926040_121219'},
  '30372': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [0, 24, 6], 'edfname': '01298381_281016'},
  '04140': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00502785_041019'},
  '26936': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01235034_120220'},
  '30461': {'GT': 1, 'Acc': ' 53.33%', 'Pred': [1, 16, 13], 'edfname': '01299709_090419'},
  '02329': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '00343454_020414'},
  '06181': {'GT': 1, 'Acc': ' 76.67%', 'Pred': [7, 23, 0], 'edfname': '00632465_150414'},
  '12827': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '00796775_301115'},
  '26891': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01235034_021117'},
  '30484': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [0, 26, 4], 'edfname': '01299709_090419'},
  '22137': {'GT': 0, 'Acc': ' 43.33%', 'Pred': [13, 17, 0], 'edfname': '01134557_301116'},
  '14339': {'GT': 2, 'Acc': ' 86.67%', 'Pred': [0, 4, 26], 'edfname': '00856756_131119'},
  '31377': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [3, 27, 0], 'edfname': '01314786_200317'},
  '03609': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00450712_161118'},
  '13119': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '00805584_260719'},
  '24464': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '01163965_130718'},
  '05246': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00614191_150617'},
  '03840': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00480292_181215'},
  '17156': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00951066_131217'},
  '17166': {'GT': 2, 'Acc': ' 70.00%', 'Pred': [1, 8, 21], 'edfname': '00951066_131217'},
  '14616': {'GT': 1, 'Acc': ' 23.33%', 'Pred': [23, 7, 0], 'edfname': '00867679_291117'},
  '03482': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00430821_100217'},
  '34085': {'GT': 1, 'Acc': ' 46.67%', 'Pred': [0, 14, 16], 'edfname': '01372947_130318'},
  '34459': {'GT': 0, 'Acc': ' 53.33%', 'Pred': [16, 14, 0], 'edfname': '01382697_261218'},
  '10544': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00735258_051218'},
  '26470': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '01216470_141118'},
  '25587': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01200248_290120'},
  '00673': {'GT': 1, 'Acc': ' 23.33%', 'Pred': [0, 7, 23], 'edfname': '00164098_021019'},
  '31060': {'GT': 1, 'Acc': ' 43.33%', 'Pred': [17, 13, 0], 'edfname': '01307080_060117'},
  '31636': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01321744_130417'},
  '14281': {'GT': 1, 'Acc': ' 73.33%', 'Pred': [4, 22, 4], 'edfname': '00852650_170818'},
  '20388': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01057134_310316'},
  '00828': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [5, 25, 0], 'edfname': '00164098_250416'},
  '02923': {'GT': 0, 'Acc': ' 63.33%', 'Pred': [19, 11, 0], 'edfname': '00399896_180414'},
  '05566': {'GT': 1, 'Acc': ' 56.67%', 'Pred': [0, 17, 13], 'edfname': '00619387_070317'},
  '14637': {'GT': 1, 'Acc': ' 36.67%', 'Pred': [19, 11, 0], 'edfname': '00869351_160217'},
  '33643': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [0, 28, 2], 'edfname': '01364159_110118'},
  '12764': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00796686_090819'},
  '18981': {'GT': 2, 'Acc': ' 46.67%', 'Pred': [14, 2, 14], 'edfname': '01000945_250615'},
  '28126': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '01254424_120118'},
  '26117': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01209182_270415'},
  '19530': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 0, 2], 'edfname': '01020014_151215'},
  '08815': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [3, 27, 0], 'edfname': '00690996_100817'},
  '13360': {'GT': 2, 'Acc': ' 80.00%', 'Pred': [0, 6, 24], 'edfname': '00816768_260618'},
  '03633': {'GT': 0, 'Acc': ' 60.00%', 'Pred': [18, 1, 11], 'edfname': '00456090_140417'},
  '28110': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 0, 2], 'edfname': '01254379_210116'},
  '11219': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00752398_110214'},
  '05864': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00626957_010617'},
  '24130': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01155763_120514'},
  '35776': {'GT': 1, 'Acc': ' 60.00%', 'Pred': [0, 18, 12], 'edfname': '01418070_200819'},
  '01800': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00293228_120718'},
  '28558': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01266696_300316'},
  '03749': {'GT': 1, 'Acc': ' 76.67%', 'Pred': [3, 23, 4], 'edfname': '00474121_161118'},
  '29140': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [0, 25, 5], 'edfname': '01277867_160616'},
  '07483': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00663561_030414'},
  '14428': {'GT': 2, 'Acc': '  3.33%', 'Pred': [28, 1, 1], 'edfname': '00860983_271114'},
  '23718': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01149087_170414'},
  '24489': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 5, 0], 'edfname': '01163965_130718'},
  '14686': {'GT': 1, 'Acc': ' 76.67%', 'Pred': [7, 23, 0], 'edfname': '00871822_070918'},
  '30937': {'GT': 0, 'Acc': ' 43.33%', 'Pred': [13, 16, 1], 'edfname': '01306520_200117'},
  '01362': {'GT': 0, 'Acc': ' 26.67%', 'Pred': [8, 2, 20], 'edfname': '00250122_100519'},
  '18734': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00987008_290318'},
  '00055': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00029426_020817'},
  '17270': {'GT': 2, 'Acc': ' 73.33%', 'Pred': [3, 5, 22], 'edfname': '00952170_060516'},
  '13954': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00840844_071118'},
  '25372': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01195277_011119'},
  '25783': {'GT': 0, 'Acc': ' 76.67%', 'Pred': [23, 7, 0], 'edfname': '01201484_150618'},
  '19358': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01011922_270815'},
  '08603': {'GT': 0, 'Acc': ' 36.67%', 'Pred': [11, 19, 0], 'edfname': '00685248_060515'},
  '36441': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '01464624_011119'},
  '11358': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [0, 27, 3], 'edfname': '00753842_110517'},
  '01934': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00313383_180314'},
  '18762': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00987555_270717'},
  '17167': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00951066_131217'},
  '11164': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00752045_260419'},
  '03406': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00427010_301118'},
  '03035': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00406530_120419'},
  '27649': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 4, 0], 'edfname': '01243336_240117'},
  '19702': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 5, 0], 'edfname': '01026841_270116'},
  '10109': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00723144_180118'},
  '30352': {'GT': 1, 'Acc': ' 60.00%', 'Pred': [12, 18, 0], 'edfname': '01298112_271016'},
  '06355': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00635370_170220'},
  '33011': {'GT': 1, 'Acc': ' 73.33%', 'Pred': [0, 22, 8], 'edfname': '01350206_021117'},
  '12760': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00796686_090819'},
  '13990': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00840844_250119'},
  '27559': {'GT': 1, 'Acc': ' 16.67%', 'Pred': [25, 5, 0], 'edfname': '01242982_110416'},
  '34222': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01376302_290318'},
  '07313': {'GT': 2, 'Acc': ' 70.00%', 'Pred': [2, 7, 21], 'edfname': '00658017_180917'},
  '25980': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01207872_281118'},
  '03761': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00474121_161118'},
  '05437': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00617308_021216'},
  '11114': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00747390_130918'},
  '05791': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 0, 5], 'edfname': '00625454_251116'},
  '05885': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00626957_040417'},
  '19487': {'GT': 0, 'Acc': ' 80.00%', 'Pred': [24, 1, 5], 'edfname': '01018913_161018'},
  '04004': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 2, 1], 'edfname': '00489204_290116'},
  '30276': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01297715_101116'},
  '12246': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [1, 0, 29], 'edfname': '00777577_120314'},
  '36203': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01432133_160519'},
  '18642': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00986061_130716'},
  '11762': {'GT': 2, 'Acc': ' 73.33%', 'Pred': [2, 6, 22], 'edfname': '00763580_281118'},
  '25710': {'GT': 1, 'Acc': ' 20.00%', 'Pred': [24, 6, 0], 'edfname': '01201052_060217'},
  '29748': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01288482_220816'},
  '32936': {'GT': 1, 'Acc': ' 66.67%', 'Pred': [0, 20, 10], 'edfname': '01348158_231019'},
  '20343': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '01056827_240818'},
  '21954': {'GT': 2, 'Acc': ' 53.33%', 'Pred': [7, 7, 16], 'edfname': '01133652_250216'},
  '00676': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00164098_021019'},
  '23271': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '01145402_161117'},
  '00769': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00164098_180919'},
  '34106': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [0, 26, 4], 'edfname': '01372947_240518'},
  '06090': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00631178_110517'},
  '28982': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 1, 1], 'edfname': '01274934_190516'},
  '20751': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01074533_071116'},
  '14202': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00851302_261217'},
  '32912': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01348158_231019'},
  '18177': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [0, 2, 28], 'edfname': '00971612_150317'},
  '26066': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01209181_250315'},
  '07977': {'GT': 1, 'Acc': ' 36.67%', 'Pred': [0, 11, 19], 'edfname': '00673505_020419'},
  '21591': {'GT': 2, 'Acc': ' 73.33%', 'Pred': [6, 2, 22], 'edfname': '01126210_280414'},
  '18230': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 0, 1], 'edfname': '00977111_211218'},
  '08692': {'GT': 1, 'Acc': '  0.00%', 'Pred': [0, 0, 30], 'edfname': '00687089_011216'},
  '27959': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01252396_141216'},
  '21967': {'GT': 2, 'Acc': ' 30.00%', 'Pred': [1, 20, 9], 'edfname': '01133652_250216'},
  '14474': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00861148_300119'},
  '18101': {'GT': 1, 'Acc': ' 23.33%', 'Pred': [13, 7, 10], 'edfname': '00967966_110517'},
  '26750': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01225385_060815'},
  '34367': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01381606_160518'},
  '20659': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01071934_070515'},
  '33079': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01351393_101019'},
  '02855': {'GT': 2, 'Acc': ' 76.67%', 'Pred': [0, 7, 23], 'edfname': '00397252_011018'},
  '15108': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [0, 27, 3], 'edfname': '00885992_011216'},
  '19387': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '01011922_290715'},
  '23534': {'GT': 0, 'Acc': ' 70.00%', 'Pred': [21, 5, 4], 'edfname': '01147100'},
  '25880': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01203444_240719'},
  '30930': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '01306496_130117'},
  '35072': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [0, 25, 5], 'edfname': '01402378_110219'},
  '18290': {'GT': 0, 'Acc': ' 33.33%', 'Pred': [10, 8, 12], 'edfname': '00978066_170815'},
  '18985': {'GT': 2, 'Acc': ' 16.67%', 'Pred': [24, 1, 5], 'edfname': '01000945_250615'},
  '25379': {'GT': 2, 'Acc': ' 73.33%', 'Pred': [0, 8, 22], 'edfname': '01195277_011119'},
  '18358': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [0, 28, 2], 'edfname': '00978569_110119'},
  '35902': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01421529_150519'},
  '03611': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '00450712_161118'},
  '26439': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [2, 26, 2], 'edfname': '01216443_240518'},
  '30593': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01301982_151116'},
  '36213': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01432133_160519'},
  '21031': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01090903_250917'},
  '18250': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 0, 1], 'edfname': '00977111_211218'},
  '05937': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00626957_250216'},
  '26284': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01212635_270515'},
  '02898': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00397252_160119'},
  '01595': {'GT': 1, 'Acc': '  6.67%', 'Pred': [28, 2, 0], 'edfname': '00286931_201217'},
  '23024': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01141785_130214'},
  '08608': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '00685248_060515'},
  '09166': {'GT': 2, 'Acc': ' 70.00%', 'Pred': [0, 9, 21], 'edfname': '00699594_011217'},
  '16461': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00926040_121219'},
  '24461': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [1, 25, 4], 'edfname': '01163321_210918'},
  '14759': {'GT': 0, 'Acc': ' 70.00%', 'Pred': [21, 9, 0], 'edfname': '00875320_070415'},
  '11061': {'GT': 2, 'Acc': ' 83.33%', 'Pred': [0, 5, 25], 'edfname': '00745209_220916'},
  '27492': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01242663_011215'},
  '05802': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 0, 4], 'edfname': '00625454_251116'},
  '10237': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00727364_231118'},
  '35759': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [0, 27, 3], 'edfname': '01418070_200819'},
  '03223': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00414635_240414'},
  '18304': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '00978066_170815'},
  '25772': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01201484_150618'},
  '27561': {'GT': 1, 'Acc': ' 26.67%', 'Pred': [21, 8, 1], 'edfname': '01242982_110416'},
  '06941': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00649687_170314'},
  '33832': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '01367137_060318'},
  '06130': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00631526_070716'},
  '14171': {'GT': 1, 'Acc': ' 16.67%', 'Pred': [25, 5, 0], 'edfname': '00850889_290817'},
  '32873': {'GT': 1, 'Acc': ' 23.33%', 'Pred': [19, 7, 4], 'edfname': '01348158_061119'},
  '20821': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '01080162_120815'},
  '08568': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [0, 27, 3], 'edfname': '00684846_130417'},
  '28071': {'GT': 1, 'Acc': ' 56.67%', 'Pred': [13, 17, 0], 'edfname': '01253550_240519'},
  '14810': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00875350_140514'},
  '06644': {'GT': 0, 'Acc': ' 13.33%', 'Pred': [4, 26, 0], 'edfname': '00643352_261218'},
  '23917': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01154140_240414'},
  '07383': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '00659706_220218'},
  '05789': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00625454_251116'},
  '14713': {'GT': 0, 'Acc': ' 56.67%', 'Pred': [17, 13, 0], 'edfname': '00872871_140618'},
  '18964': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00996941_180517'},
  '20826': {'GT': 0, 'Acc': ' 80.00%', 'Pred': [24, 6, 0], 'edfname': '01080162_120815'},
  '21900': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [1, 28, 1], 'edfname': '01132092_171017'},
  '22377': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '01137650_290819'},
  '27068': {'GT': 0, 'Acc': ' 76.67%', 'Pred': [23, 6, 1], 'edfname': '01235273_041115'},
  '18426': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '00980362_020616'},
  '03458': {'GT': 2, 'Acc': ' 63.33%', 'Pred': [6, 5, 19], 'edfname': '00430821_031116'},
  '15926': {'GT': 2, 'Acc': ' 73.33%', 'Pred': [0, 8, 22], 'edfname': '00906868_071216'},
  '05380': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '00616379_010915'},
  '02926': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00399896_180414'},
  '34320': {'GT': 2, 'Acc': ' 36.67%', 'Pred': [0, 19, 11], 'edfname': '01379199_220618'},
  '34939': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01400560_160419'},
  '34055': {'GT': 2, 'Acc': ' 86.67%', 'Pred': [0, 4, 26], 'edfname': '01371418_090318'},
  '30668': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [0, 24, 6], 'edfname': '01303196_020317'},
  '25151': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 2, 1], 'edfname': '01186934_200519'},
  '04177': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [2, 0, 28], 'edfname': '00502785_140316'},
  '23328': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01145493_030414'},
  '26950': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01235034_260220'},
  '31476': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01318352_281118'},
  '32079': {'GT': 2, 'Acc': ' 86.67%', 'Pred': [1, 3, 26], 'edfname': '01334756_191217'},
  '02705': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00389465_040517'},
  '02022': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00316358_070715'},
  '34099': {'GT': 1, 'Acc': ' 76.67%', 'Pred': [0, 23, 7], 'edfname': '01372947_240518'},
  '11592': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00760504_080618'},
  '31363': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01314783_100417'},
  '14340': {'GT': 2, 'Acc': ' 40.00%', 'Pred': [0, 18, 12], 'edfname': '00856756_131119'},
  '11678': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00762439_081217'},
  '20479': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [0, 2, 28], 'edfname': '01064650_161117'},
  '23981': {'GT': 0, 'Acc': ' 56.67%', 'Pred': [17, 13, 0], 'edfname': '01154834_011019'},
  '23985': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01154834_180417'},
  '34754': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01393410_160818'},
  '18470': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 0, 5], 'edfname': '00983533_290618'},
  '18089': {'GT': 1, 'Acc': ' 20.00%', 'Pred': [24, 6, 0], 'edfname': '00967966_110517'},
  '15597': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 0, 3], 'edfname': '00896386_240914'},
  '12934': {'GT': 2, 'Acc': ' 53.33%', 'Pred': [12, 2, 16], 'edfname': '00803113_261115'},
  '17851': {'GT': 2, 'Acc': ' 50.00%', 'Pred': [1, 14, 15], 'edfname': '00960161_201014'},
  '12338': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '00779511_050319'},
  '10874': {'GT': 0, 'Acc': ' 43.33%', 'Pred': [13, 17, 0], 'edfname': '00742152_051016'},
  '31085': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [1, 28, 1], 'edfname': '01307883_100217'},
  '14199': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [5, 25, 0], 'edfname': '00851302_261217'},
  '23988': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01154834_180417'},
  '20893': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01081922_071215'},
  '19177': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01007924_300419'},
  '29899': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01291791_100217'},
  '14283': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [4, 26, 0], 'edfname': '00852650_170818'},
  '31350': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01314783_100417'},
  '08557': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00684846_130417'},
  '01304': {'GT': 1, 'Acc': ' 60.00%', 'Pred': [12, 18, 0], 'edfname': '00244987_050315'},
  '20075': {'GT': 2, 'Acc': ' 60.00%', 'Pred': [6, 6, 18], 'edfname': '01036861_090618'},
  '14140': {'GT': 2, 'Acc': ' 70.00%', 'Pred': [0, 9, 21], 'edfname': '00848071_121018'},
  '23944': {'GT': 0, 'Acc': ' 23.33%', 'Pred': [7, 20, 3], 'edfname': '01154528_150514'},
  '33563': {'GT': 1, 'Acc': ' 20.00%', 'Pred': [0, 6, 24], 'edfname': '01361792_301018'},
  '04082': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '00498791_260916'},
  '34719': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01392885_240718'},
  '32890': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [4, 26, 0], 'edfname': '01348158_131217'},
  '01819': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 5, 0], 'edfname': '00295596_101016'},
  '04275': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 0, 2], 'edfname': '00507286_270715'},
  '12834': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00796775_301115'},
  '18429': {'GT': 0, 'Acc': ' 80.00%', 'Pred': [24, 6, 0], 'edfname': '00980362_020616'},
  '10662': {'GT': 2, 'Acc': ' 86.67%', 'Pred': [1, 3, 26], 'edfname': '00739162_141117'},
  '17699': {'GT': 0, 'Acc': ' 26.67%', 'Pred': [8, 22, 0], 'edfname': '00957827_230317'},
  '26084': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01209181_270415'},
  '22779': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01139940_290114'},
  '29201': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01277998_220217'},
  '07921': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00672867_031116'},
  '03386': {'GT': 0, 'Acc': ' 33.33%', 'Pred': [10, 20, 0], 'edfname': '00422970_151118'},
  '32929': {'GT': 1, 'Acc': ' 50.00%', 'Pred': [0, 15, 15], 'edfname': '01348158_231019'},
  '15923': {'GT': 2, 'Acc': ' 90.00%', 'Pred': [0, 3, 27], 'edfname': '00906868_071216'},
  '06901': {'GT': 2, 'Acc': ' 83.33%', 'Pred': [5, 0, 25], 'edfname': '00649687_010715'},
  '07587': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00665821_210715'},
  '24226': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01156960_220514'},
  '15229': {'GT': 0, 'Acc': ' 10.00%', 'Pred': [3, 27, 0], 'edfname': '00887981_210617'},
  '16222': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '00916290_270617'},
  '21400': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '01116389_271118'},
  '36411': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '01457795_141019'},
  '00134': {'GT': 2, 'Acc': ' 66.67%', 'Pred': [1, 9, 20], 'edfname': '00048377_070916'},
  '12465': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '00784417_100315'},
  '20715': {'GT': 1, 'Acc': ' 13.33%', 'Pred': [23, 4, 3], 'edfname': '01073130_191018'},
  '20677': {'GT': 1, 'Acc': ' 46.67%', 'Pred': [16, 14, 0], 'edfname': '01073130_101018'},
  '22490': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01138298_220114'},
  '32028': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 3, 2], 'edfname': '01334570_070717'},
  '35939': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01422773_010419'},
  '34477': {'GT': 0, 'Acc': ' 53.33%', 'Pred': [16, 14, 0], 'edfname': '01382697_261218'},
  '27853': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [6, 24, 0], 'edfname': '01251650_060116'},
  '16214': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [3, 26, 1], 'edfname': '00916290_270617'},
  '07493': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [2, 0, 28], 'edfname': '00663561_030414'},
  '08881': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [0, 28, 2], 'edfname': '00693392_210617'},
  '05872': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [0, 2, 28], 'edfname': '00626957_040417'},
  '23416': {'GT': 0, 'Acc': ' 46.67%', 'Pred': [14, 16, 0], 'edfname': '01147097_121119'},
  '25544': {'GT': 2, 'Acc': ' 76.67%', 'Pred': [2, 5, 23], 'edfname': '01200248_100215'},
  '26575': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01220269_141218'},
  '06526': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '00641692_170315'},
  '29099': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '01276868_020616'},
  '02750': {'GT': 0, 'Acc': ' 70.00%', 'Pred': [21, 9, 0], 'edfname': '00395374_120314'},
  '28140': {'GT': 0, 'Acc': ' 33.33%', 'Pred': [10, 16, 4], 'edfname': '01254424_120118'},
  '13624': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00824216_080416'},
  '25424': {'GT': 2, 'Acc': ' 86.67%', 'Pred': [4, 0, 26], 'edfname': '01195277_291215'},
  '05563': {'GT': 1, 'Acc': ' 10.00%', 'Pred': [2, 3, 25], 'edfname': '00619387_070317'},
  '06672': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00643806_130718'},
  '01802': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [5, 25, 0], 'edfname': '00293228_120718'},
  '29862': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 2, 2], 'edfname': '01290676_090617'},
  '16586': {'GT': 1, 'Acc': ' 50.00%', 'Pred': [13, 15, 2], 'edfname': '00931073_181018'},
  '15660': {'GT': 2, 'Acc': ' 23.33%', 'Pred': [6, 17, 7], 'edfname': '00899380_060515'},
  '12432': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00783510_301115'},
  '00715': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '00164098_041214'},
  '02963': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 3, 1], 'edfname': '00400036_120314'},
  '25266': {'GT': 0, 'Acc': '  6.67%', 'Pred': [2, 28, 0], 'edfname': '01191314_020317'},
  '22185': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01135534_230715'},
  '10538': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00735258_051218'},
  '13278': {'GT': 0, 'Acc': ' 56.67%', 'Pred': [17, 13, 0], 'edfname': '00813176_030718'},
  '08132': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00675516_090120'},
  '06881': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00649687_010715'},
  '31959': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01332707_220817'},
  '25909': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01203444_251017'},
  '02291': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00335823_190815'},
  '07423': {'GT': 2, 'Acc': ' 60.00%', 'Pred': [0, 12, 18], 'edfname': '00661209_010816'},
  '29548': {'GT': 1, 'Acc': ' 60.00%', 'Pred': [0, 18, 12], 'edfname': '01284214_160816'},
  '01986': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00315506_250715'},
  '22710': {'GT': 2, 'Acc': ' 76.67%', 'Pred': [5, 2, 23], 'edfname': '01139924_300315'},
  '20271': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [0, 2, 28], 'edfname': '01053391_120618'},
  '08538': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00684846_030717'},
  '02204': {'GT': 0, 'Acc': '  3.33%', 'Pred': [1, 8, 21], 'edfname': '00329526_161118'},
  '07822': {'GT': 0, 'Acc': ' 46.67%', 'Pred': [14, 16, 0], 'edfname': '00671212_141016'},
  '18511': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00984999_021117'},
  '18579': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [2, 27, 1], 'edfname': '00985401_011117'},
  '15434': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [5, 25, 0], 'edfname': '00891889_060717'},
  '32351': {'GT': 1, 'Acc': ' 76.67%', 'Pred': [0, 23, 7], 'edfname': '01338642_081119'},
  '00982': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [1, 0, 29], 'edfname': '00213418_210115'},
  '33162': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01351393_261017'},
  '25044': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 0, 5], 'edfname': '01182608_201014'},
  '33015': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [2, 27, 1], 'edfname': '01350206_021117'},
  '22037': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01134450_140519'},
  '05082': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00609255_140417'},
  '28066': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01253550_240519'},
  '16394': {'GT': 1, 'Acc': ' 30.00%', 'Pred': [21, 9, 0], 'edfname': '00919596_200117'},
  '06754': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00646518_240217'},
  '20188': {'GT': 1, 'Acc': ' 40.00%', 'Pred': [15, 12, 3], 'edfname': '01043631_211117'},
  '19709': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01026841_270116'},
  '32963': {'GT': 2, 'Acc': '  0.00%', 'Pred': [0, 30, 0], 'edfname': '01348263_210917'},
  '34722': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01392885_240718'},
  '10657': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00739162_011215'},
  '11765': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00763580_281118'},
  '01997': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00315506_250715'},
  '24335': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01159816_060515'},
  '00822': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [5, 24, 1], 'edfname': '00164098_250416'},
  '12061': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [0, 25, 5], 'edfname': '00771910_121016'},
  '02556': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00368086_111116'},
  '15948': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [0, 2, 28], 'edfname': '00906868_151214'},
  '29951': {'GT': 1, 'Acc': ' 70.00%', 'Pred': [9, 21, 0], 'edfname': '01293646_181016'},
  '14786': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00875320_190515'},
  '28403': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 5, 0], 'edfname': '01264838_110416'},
  '00812': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00164098_230317'},
  '22774': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01139940_290114'},
  '23600': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01147103_130314'},
  '08348': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00678829_160217'},
  '16338': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00919146_120517'},
  '30564': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 4, 0], 'edfname': '01300391_251116'},
  '18513': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00984999_021117'},
  '32889': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [3, 27, 0], 'edfname': '01348158_131217'},
  '13534': {'GT': 1, 'Acc': ' 13.33%', 'Pred': [4, 4, 22], 'edfname': '00817768_291118'},
  '08436': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00682484_240718'},
  '07134': {'GT': 0, 'Acc': ' 60.00%', 'Pred': [18, 12, 0], 'edfname': '00654091_080618'},
  '26887': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01235034_021117'},
  '24753': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01174220_021216'},
  '24706': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01174162_090817'},
  '20423': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01063007_171115'},
  '31469': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '01318352_281118'},
  '05135': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 1, 4], 'edfname': '00611999_041018'},
  '27892': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [6, 24, 0], 'edfname': '01251650_191219'},
  '23329': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01145493_030414'},
  '04908': {'GT': 1, 'Acc': ' 73.33%', 'Pred': [0, 22, 8], 'edfname': '00607068_260617'},
  '23372': {'GT': 1, 'Acc': ' 36.67%', 'Pred': [19, 11, 0], 'edfname': '01146683_300419'},
  '32034': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01334570_070717'},
  '23360': {'GT': 1, 'Acc': ' 36.67%', 'Pred': [19, 11, 0], 'edfname': '01146683_300419'},
  '11218': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00752398_110214'},
  '00287': {'GT': 1, 'Acc': ' 46.67%', 'Pred': [16, 14, 0], 'edfname': '00088513_120717'},
  '29525': {'GT': 1, 'Acc': ' 60.00%', 'Pred': [2, 18, 10], 'edfname': '01284214_160816'},
  '15733': {'GT': 1, 'Acc': ' 76.67%', 'Pred': [3, 23, 4], 'edfname': '00899695_280918'},
  '34664': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '01392607_260718'},
  '08683': {'GT': 1, 'Acc': ' 76.67%', 'Pred': [0, 23, 7], 'edfname': '00687089_011216'},
  '06250': {'GT': 1, 'Acc': ' 50.00%', 'Pred': [10, 15, 5], 'edfname': '00634366_150316'},
  '27962': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01252396_141216'},
  '11352': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '00753842_110517'},
  '22754': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01139939_110214'},
  '32922': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '01348158_231019'},
  '22102': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01134450_300919'},
  '14798': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '00875320_220414'},
  '30620': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01301982_230118'},
  '10124': {'GT': 0, 'Acc': ' 50.00%', 'Pred': [15, 11, 4], 'edfname': '00723232_270318'},
  '30646': {'GT': 0, 'Acc': '  0.00%', 'Pred': [0, 30, 0], 'edfname': '01302961_060117'},
  '01759': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00293228_070918'},
  '07658': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00666711_140218'},
  '18620': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [2, 27, 1], 'edfname': '00985987_180518'},
  '35332': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01409497_111219'},
  '36079': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [0, 27, 3], 'edfname': '01429076_070819'},
  '26594': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 0, 2], 'edfname': '01222061_271017'},
  '23131': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '01141790_190214'},
  '10714': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [0, 2, 28], 'edfname': '00739864_070717'},
  '06103': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00631178_110517'},
  '26247': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 3, 2], 'edfname': '01211503_120515'},
  '24333': {'GT': 0, 'Acc': ' 60.00%', 'Pred': [18, 12, 0], 'edfname': '01159816_060515'},
  '10223': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00727364_231118'},
  '16881': {'GT': 2, 'Acc': ' 76.67%', 'Pred': [1, 6, 23], 'edfname': '00945146_011214'},
  '11050': {'GT': 2, 'Acc': ' 63.33%', 'Pred': [0, 11, 19], 'edfname': '00745209_220916'},
  '18794': {'GT': 2, 'Acc': ' 90.00%', 'Pred': [0, 3, 27], 'edfname': '00988278_020819'},
  '11466': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [1, 25, 4], 'edfname': '00758349_270418'},
  '03997': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00487637_220715'},
  '19340': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '01011144_070814'},
  '20861': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '01081922_051115'},
  '05218': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '00613313_230518'},
  '08701': {'GT': 1, 'Acc': ' 30.00%', 'Pred': [0, 9, 21], 'edfname': '00687089_011216'},
  '28957': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01274934'},
  '10008': {'GT': 1, 'Acc': ' 46.67%', 'Pred': [1, 14, 15], 'edfname': '00722305_261018'},
  '13397': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '00817022_010415'},
  '01969': {'GT': 2, 'Acc': ' 63.33%', 'Pred': [0, 11, 19], 'edfname': '00314911_101218'},
  '19795': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [0, 2, 28], 'edfname': '01027623_040416'},
  '24891': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01179631_170914'},
  '24096': {'GT': 0, 'Acc': ' 66.67%', 'Pred': [20, 10, 0], 'edfname': '01155731_070514'},
  '22192': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01135534_230715'},
  '30205': {'GT': 1, 'Acc': ' 70.00%', 'Pred': [0, 21, 9], 'edfname': '01296557_010819'},
  '28773': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 4, 0], 'edfname': '01271175_270416'},
  '04329': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 1, 3], 'edfname': '00518633_311215'},
  '12458': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [0, 28, 2], 'edfname': '00784417_100315'},
  '21004': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01090903_250917'},
  '27686': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01246499_301115'},
  '09859': {'GT': 2, 'Acc': ' 63.33%', 'Pred': [2, 9, 19], 'edfname': '00717883_260814'},
  '03632': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00456090_140417'},
  '35435': {'GT': 0, 'Acc': ' 43.33%', 'Pred': [13, 17, 0], 'edfname': '01410532_030419'},
  '06482': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00639879_061218'},
  '26234': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 4, 0], 'edfname': '01211503_070415'},
  '02936': {'GT': 0, 'Acc': ' 76.67%', 'Pred': [23, 7, 0], 'edfname': '00399896_180414'},
  '25837': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [0, 24, 6], 'edfname': '01203444_090819'},
  '32357': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01338642_081119'},
  '25012': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01182545_070316'},
  '24831': {'GT': 2, 'Acc': ' 46.67%', 'Pred': [5, 11, 14], 'edfname': '01178797_240914'},
  '05018': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '00608961_131118'},
  '05938': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00626957_250216'},
  '11670': {'GT': 2, 'Acc': ' 86.67%', 'Pred': [4, 0, 26], 'edfname': '00762439_081217'},
  '33110': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '01351393_231019'},
  '35059': {'GT': 1, 'Acc': ' 26.67%', 'Pred': [7, 8, 15], 'edfname': '01402378_110219'},
  '13817': {'GT': 2, 'Acc': ' 60.00%', 'Pred': [0, 12, 18], 'edfname': '00839002_270218'},
  '30339': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [6, 24, 0], 'edfname': '01298112_271016'},
  '29561': {'GT': 2, 'Acc': ' 16.67%', 'Pred': [21, 4, 5], 'edfname': '01285216_140717'},
  '11095': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00747390_130918'},
  '14629': {'GT': 1, 'Acc': ' 40.00%', 'Pred': [18, 12, 0], 'edfname': '00869351_160217'},
  '30162': {'GT': 1, 'Acc': ' 46.67%', 'Pred': [16, 14, 0], 'edfname': '01296344_211116'},
  '04526': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00601765_231118'},
  '21673': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01128572_120314'},
  '01839': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00298616_290917'},
  '35341': {'GT': 2, 'Acc': ' 90.00%', 'Pred': [0, 3, 27], 'edfname': '01409497_111219'},
  '11890': {'GT': 1, 'Acc': ' 56.67%', 'Pred': [13, 17, 0], 'edfname': '00766705_230118'},
  '07139': {'GT': 0, 'Acc': ' 60.00%', 'Pred': [18, 12, 0], 'edfname': '00654091_080618'},
  '09471': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00708411_091118'},
  '07637': {'GT': 2, 'Acc': ' 70.00%', 'Pred': [2, 7, 21], 'edfname': '00666433_230818'},
  '07008': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [3, 27, 0], 'edfname': '00650465_280616'},
  '23945': {'GT': 0, 'Acc': ' 63.33%', 'Pred': [19, 11, 0], 'edfname': '01154528_150514'},
  '05376': {'GT': 2, 'Acc': ' 73.33%', 'Pred': [0, 8, 22], 'edfname': '00616193_090218'},
  '27021': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01235272_051115'},
  '25908': {'GT': 1, 'Acc': ' 33.33%', 'Pred': [0, 10, 20], 'edfname': '01203444_251017'},
  '20133': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [1, 26, 3], 'edfname': '01041208_020818'},
  '14711': {'GT': 0, 'Acc': ' 43.33%', 'Pred': [13, 17, 0], 'edfname': '00872871_140618'},
  '17025': {'GT': 2, 'Acc': ' 73.33%', 'Pred': [0, 8, 22], 'edfname': '00949701_180219'},
  '33004': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [2, 27, 1], 'edfname': '01350206_021117'},
  '00986': {'GT': 2, 'Acc': ' 80.00%', 'Pred': [6, 0, 24], 'edfname': '00213418_210115'},
  '14163': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00848071_121018'},
  '27417': {'GT': 1, 'Acc': ' 13.33%', 'Pred': [0, 4, 26], 'edfname': '01242449_230617'},
  '01452': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00258008_230617'},
  '03045': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00406530_120419'},
  '29956': {'GT': 1, 'Acc': ' 23.33%', 'Pred': [23, 7, 0], 'edfname': '01293646_181016'},
  '06225': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00633563_061015'},
  '04446': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00532450_220317'},
  '30740': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '01303263_281116'},
  '07485': {'GT': 2, 'Acc': ' 80.00%', 'Pred': [6, 0, 24], 'edfname': '00663561_030414'},
  '20678': {'GT': 1, 'Acc': ' 13.33%', 'Pred': [26, 4, 0], 'edfname': '01073130_101018'},
  '01463': {'GT': 0, 'Acc': ' 60.00%', 'Pred': [18, 12, 0], 'edfname': '00265504_101016'},
  '20951': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01090425_290419'},
  '35283': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01408549_031218'},
  '03542': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [3, 26, 1], 'edfname': '00433859_230519'},
  '33673': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01364379_230118'},
  '22146': {'GT': 0, 'Acc': ' 10.00%', 'Pred': [3, 27, 0], 'edfname': '01134557_301116'},
  '00026': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00029426_020817'},
  '31223': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01311315_230217'},
  '07675': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00666711_150316'},
  '15072': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [4, 24, 2], 'edfname': '00885959_160419'},
  '32492': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01339759_071119'},
  '15294': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '00888613_110518'},
  '05391': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00616379_010915'},
  '08607': {'GT': 0, 'Acc': ' 46.67%', 'Pred': [14, 0, 16], 'edfname': '00685248_060515'},
  '03020': {'GT': 0, 'Acc': ' 36.67%', 'Pred': [11, 19, 0], 'edfname': '00405960_240414'},
  '05213': {'GT': 1, 'Acc': ' 60.00%', 'Pred': [4, 18, 8], 'edfname': '00613313_230518'},
  '35906': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01421529_150519'},
  '16247': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00916546_281114'},
  '21484': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01124522_170818'},
  '23412': {'GT': 0, 'Acc': ' 40.00%', 'Pred': [12, 18, 0], 'edfname': '01147097_121119'},
  '29073': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01276737_300616'},
  '15275': {'GT': 1, 'Acc': ' 70.00%', 'Pred': [9, 21, 0], 'edfname': '00888613_080618'},
  '16391': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '00919596_200117'},
  '25863': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '01203444_240719'},
  '28348': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '01262206_100419'},
  '27412': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01241989_101115'},
  '07403': {'GT': 1, 'Acc': ' 46.67%', 'Pred': [16, 14, 0], 'edfname': '00660468_191214'},
  '07098': {'GT': 1, 'Acc': ' 66.67%', 'Pred': [2, 20, 8], 'edfname': '00651389_281016'},
  '21629': {'GT': 1, 'Acc': ' 23.33%', 'Pred': [4, 7, 19], 'edfname': '01127836_221116'},
  '10126': {'GT': 0, 'Acc': ' 30.00%', 'Pred': [9, 16, 5], 'edfname': '00723232_270318'},
  '07487': {'GT': 2, 'Acc': ' 33.33%', 'Pred': [9, 11, 10], 'edfname': '00663561_030414'},
  '32473': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01339759_071119'},
  '05108': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00611497_030817'},
  '27106': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01235281_191015'},
  '13485': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [6, 24, 0], 'edfname': '00817092_020118'},
  '18292': {'GT': 0, 'Acc': ' 10.00%', 'Pred': [3, 27, 0], 'edfname': '00978066_170815'},
  '12270': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00777684_300119'},
  '00029': {'GT': 0, 'Acc': ' 66.67%', 'Pred': [20, 9, 1], 'edfname': '00029426_020817'},
  '06311': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00634467_180717'},
  '05147': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '00612060_221118'},
  '04338': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00521264_170815'},
  '26202': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '01211467_070415'},
  '11211': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00752398_110214'},
  '08880': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00692685_200919'},
  '32129': {'GT': 2, 'Acc': ' 73.33%', 'Pred': [4, 4, 22], 'edfname': '01335435_121119'},
  '00791': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '00164098_230317'},
  '18982': {'GT': 2, 'Acc': '  6.67%', 'Pred': [2, 26, 2], 'edfname': '01000945_250615'},
  '05091': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00611497_030817'},
  '32348': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '01338642_081119'},
  '17207': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00951808_251116'},
  '24302': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01159187_131118'},
  '28180': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 4, 0], 'edfname': '01256391_310316'},
  '34486': {'GT': 0, 'Acc': ' 13.33%', 'Pred': [4, 25, 1], 'edfname': '01382697_261218'},
  '34390': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '01381798_100818'},
  '15273': {'GT': 1, 'Acc': ' 70.00%', 'Pred': [9, 21, 0], 'edfname': '00888613_080618'},
  '00897': {'GT': 1, 'Acc': ' 33.33%', 'Pred': [14, 10, 6], 'edfname': '00195824_201017'},
  '21118': {'GT': 0, 'Acc': ' 70.00%', 'Pred': [21, 9, 0], 'edfname': '01096498_270718'},
  '00888': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '00195824_201017'},
  '24435': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '01163321_190718'},
  '13004': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '00803780_071218'},
  '23592': {'GT': 0, 'Acc': ' 80.00%', 'Pred': [24, 6, 0], 'edfname': '01147103_130314'},
  '08374': {'GT': 1, 'Acc': ' 40.00%', 'Pred': [11, 12, 7], 'edfname': '00678916_020718'},
  '23729': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [0, 26, 4], 'edfname': '01150270_020617'},
  '26585': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [0, 25, 5], 'edfname': '01220269_141218'},
  '28820': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01271298_270319'},
  '33022': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01350206_021117'},
  '00829': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00164098_250416'},
  '13445': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 1, 1], 'edfname': '00817054_100918'},
  '14210': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '00851302_261217'},
  '09870': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '00718764_270715'},
  '30217': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01296557_010819'},
  '01585': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00285244_020414'},
  '08220': {'GT': 2, 'Acc': ' 46.67%', 'Pred': [10, 6, 14], 'edfname': '00675516_270214'},
  '34798': {'GT': 0, 'Acc': ' 76.67%', 'Pred': [23, 2, 5], 'edfname': '01395459_210819'},
  '17704': {'GT': 0, 'Acc': ' 50.00%', 'Pred': [15, 15, 0], 'edfname': '00957827_230317'},
  '33112': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01351393_231019'},
  '20337': {'GT': 2, 'Acc': ' 86.67%', 'Pred': [0, 4, 26], 'edfname': '01056827_240818'},
  '07805': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 1, 3], 'edfname': '00671212_141016'},
  '22830': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01139942_060214'},
  '03835': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00480292_181215'},
  '30148': {'GT': 1, 'Acc': '  0.00%', 'Pred': [30, 0, 0], 'edfname': '01296344_211116'},
  '18770': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [5, 25, 0], 'edfname': '00987555_270717'},
  '31825': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01329167_211218'},
  '36320': {'GT': 2, 'Acc': ' 40.00%', 'Pred': [5, 13, 12], 'edfname': '01446092_160819'},
  '24379': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01159816_310315'},
  '10588': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00736916_310316'},
  '31185': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01308862_130117'},
  '22294': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01136368_160114'},
  '13363': {'GT': 2, 'Acc': ' 86.67%', 'Pred': [0, 4, 26], 'edfname': '00816768_260618'},
  '16753': {'GT': 2, 'Acc': ' 30.00%', 'Pred': [2, 19, 9], 'edfname': '00939852_140214'},
  '02188': {'GT': 0, 'Acc': ' 46.67%', 'Pred': [14, 2, 14], 'edfname': '00329526_161118'},
  '11129': {'GT': 0, 'Acc': ' 50.00%', 'Pred': [15, 15, 0], 'edfname': '00747390_250718'},
  '10640': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00739162_011215'},
  '36368': {'GT': 2, 'Acc': ' 36.67%', 'Pred': [0, 19, 11], 'edfname': '01453571_270819'},
  '33480': {'GT': 1, 'Acc': ' 73.33%', 'Pred': [8, 22, 0], 'edfname': '01358607_010818'},
  '23237': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 0, 4], 'edfname': '01144046_260517'},
  '15714': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00899695_240718'},
  '13983': {'GT': 0, 'Acc': ' 76.67%', 'Pred': [23, 7, 0], 'edfname': '00840844_250119'},
  '05170': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00613032_281215'},
  '35023': {'GT': 0, 'Acc': ' 23.33%', 'Pred': [7, 16, 7], 'edfname': '01402179_311018'},
  '32906': {'GT': 1, 'Acc': ' 53.33%', 'Pred': [10, 16, 4], 'edfname': '01348158_131217'},
  '09895': {'GT': 0, 'Acc': ' 30.00%', 'Pred': [9, 21, 0], 'edfname': '00720119_200318'},
  '10782': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 0, 5], 'edfname': '00740750_110315'},
  '33879': {'GT': 2, 'Acc': ' 86.67%', 'Pred': [0, 4, 26], 'edfname': '01367495_310118'},
  '01823': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00295596_101016'},
  '03797': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [2, 0, 28], 'edfname': '00480292_130616'},
  '25914': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01203444_251017'},
  '19992': {'GT': 0, 'Acc': ' 43.33%', 'Pred': [13, 17, 0], 'edfname': '01035046_020715'},
  '04069': {'GT': 1, 'Acc': ' 50.00%', 'Pred': [15, 15, 0], 'edfname': '00492040_160516'},
  '23578': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01147102_080414'},
  '02304': {'GT': 0, 'Acc': ' 60.00%', 'Pred': [18, 12, 0], 'edfname': '00335823_220715'},
  '32005': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01333917_210918'},
  '32136': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01335435_280617'},
  '32142': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '01335435_280617'},
  '08524': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00684846_030717'},
  '22399': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01137650_290819'},
  '22819': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01139942_060214'},
  '22131': {'GT': 0, 'Acc': ' 50.00%', 'Pred': [15, 15, 0], 'edfname': '01134557_301116'},
  '29892': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01291791_100217'},
  '14150': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00848071_121018'},
  '05572': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '00619387_070317'},
  '23697': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01149084_090414'},
  '11900': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '00766705_230118'},
  '29026': {'GT': 1, 'Acc': ' 53.33%', 'Pred': [13, 16, 1], 'edfname': '01276165_040117'},
  '33824': {'GT': 0, 'Acc': '  3.33%', 'Pred': [1, 29, 0], 'edfname': '01367136_060318'},
  '07297': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00657399_070515'},
  '05615': {'GT': 1, 'Acc': ' 70.00%', 'Pred': [4, 21, 5], 'edfname': '00620551_141215'},
  '31449': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '01315776_060417'},
  '15019': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00885321_170314'},
  '07665': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 0, 1], 'edfname': '00666711_140218'},
  '25265': {'GT': 0, 'Acc': ' 70.00%', 'Pred': [21, 9, 0], 'edfname': '01191314_020317'},
  '09041': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00697249_090414'},
  '06729': {'GT': 1, 'Acc': '  3.33%', 'Pred': [29, 1, 0], 'edfname': '00645911_250315'},
  '07391': {'GT': 1, 'Acc': ' 20.00%', 'Pred': [24, 6, 0], 'edfname': '00660468_191214'},
  '04211': {'GT': 0, 'Acc': ' 46.67%', 'Pred': [14, 16, 0], 'edfname': '00505209_210715'},
  '19052': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01006707_260319'},
  '32596': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '01340109_030817'},
  '16088': {'GT': 1, 'Acc': ' 63.33%', 'Pred': [11, 19, 0], 'edfname': '00907971_030217'},
  '04999': {'GT': 0, 'Acc': ' 30.00%', 'Pred': [9, 21, 0], 'edfname': '00608961_131118'},
  '04079': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00498791_260916'},
  '17825': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00959479_090217'},
  '06300': {'GT': 1, 'Acc': ' 26.67%', 'Pred': [0, 8, 22], 'edfname': '00634366_311019'},
  '29741': {'GT': 0, 'Acc': ' 53.33%', 'Pred': [16, 8, 6], 'edfname': '01288482_220816'},
  '34641': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '01392606_260718'},
  '23227': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '01144046_260517'},
  '14538': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00865039_170816'},
  '19722': {'GT': 2, 'Acc': ' 23.33%', 'Pred': [13, 10, 7], 'edfname': '01026990_241215'},
  '33883': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [1, 0, 29], 'edfname': '01367495_310118'},
  '01498': {'GT': 0, 'Acc': ' 56.67%', 'Pred': [17, 12, 1], 'edfname': '00265634_301117'},
  '32489': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [3, 27, 0], 'edfname': '01339759_071119'},
  '15608': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00898306_010618'},
  '29407': {'GT': 0, 'Acc': ' 70.00%', 'Pred': [21, 2, 7], 'edfname': '01280762_171116'},
  '26267': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01212635_140515'},
  '00440': {'GT': 1, 'Acc': ' 56.67%', 'Pred': [13, 17, 0], 'edfname': '00127458_150618'},
  '21408': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01116389_271118'},
  '12295': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00779318_101117'},
  '05868': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00626957_010617'},
  '20250': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [3, 27, 0], 'edfname': '01044712_261016'},
  '29762': {'GT': 1, 'Acc': ' 56.67%', 'Pred': [13, 17, 0], 'edfname': '01288483_290816'},
  '23276': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [5, 25, 0], 'edfname': '01145402_161117'},
  '22039': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01134450_140519'},
  '17233': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00951861_241117'},
  '02591': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00372136_181214'},
  '28229': {'GT': 1, 'Acc': ' 70.00%', 'Pred': [1, 21, 8], 'edfname': '01260350_090316'},
  '01340': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00248994_171016'},
  '23715': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01149087_170414'},
  '17606': {'GT': 0, 'Acc': ' 76.67%', 'Pred': [23, 6, 1], 'edfname': '00956048_010715'},
  '06903': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00649687_010715'},
  '34383': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01381606_160518'},
  '35096': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01403151_080319'},
  '07257': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00657017_281218'},
  '30670': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [0, 25, 5], 'edfname': '01303196_020317'},
  '27654': {'GT': 0, 'Acc': ' 26.67%', 'Pred': [8, 22, 0], 'edfname': '01243336_240117'},
  '19739': {'GT': 2, 'Acc': '  6.67%', 'Pred': [2, 26, 2], 'edfname': '01026990_241215'},
  '11818': {'GT': 1, 'Acc': ' 56.67%', 'Pred': [1, 17, 12], 'edfname': '00764842_070514'},
  '25023': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01182545_070316'},
  '04276': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00507286_270715'},
  '16124': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '00912302_100415'},
  '24737': {'GT': 1, 'Acc': ' 70.00%', 'Pred': [7, 21, 2], 'edfname': '01174216_090516'},
  '06237': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '00634366_150316'},
  '05799': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00625454_251116'},
  '16708': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '00935598_301018'},
  '01080': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00223407_301219'},
  '18608': {'GT': 2, 'Acc': ' 90.00%', 'Pred': [0, 3, 27], 'edfname': '00985827_060718'},
  '18093': {'GT': 1, 'Acc': ' 73.33%', 'Pred': [8, 22, 0], 'edfname': '00967966_110517'},
  '10965': {'GT': 1, 'Acc': ' 46.67%', 'Pred': [0, 14, 16], 'edfname': '00744497_260517'},
  '13636': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00824216_110516'},
  '26249': {'GT': 0, 'Acc': ' 26.67%', 'Pred': [8, 6, 16], 'edfname': '01211503_120515'},
  '18079': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [4, 24, 2], 'edfname': '00967966_110517'},
  '21485': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01124522_170818'},
  '00491': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 5, 0], 'edfname': '00133390_220414'},
  '12720': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00796686_020819'},
  '24214': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01156406_120319'},
  '14750': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [0, 27, 3], 'edfname': '00873142_220319'},
  '13579': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00821910_130918'},
  '03851': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00480292_231215'},
  '03897': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [2, 27, 1], 'edfname': '00484899_260115'},
  '14424': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00860983_271114'},
  '33696': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01364379_260919'},
  '24500': {'GT': 1, 'Acc': ' 66.67%', 'Pred': [0, 20, 10], 'edfname': '01164481_220615'},
  '02891': {'GT': 2, 'Acc': '  0.00%', 'Pred': [4, 26, 0], 'edfname': '00397252_160119'},
  '06086': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00631178_110517'},
  '05061': {'GT': 2, 'Acc': ' 66.67%', 'Pred': [0, 10, 20], 'edfname': '00609255_140417'},
  '16679': {'GT': 2, 'Acc': ' 53.33%', 'Pred': [0, 14, 16], 'edfname': '00935467_100518'},
  '09611': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [0, 28, 2], 'edfname': '00713432_210519'},
  '11101': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '00747390_130918'},
  '14600': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00867679_291117'},
  '05253': {'GT': 2, 'Acc': ' 83.33%', 'Pred': [0, 5, 25], 'edfname': '00614191_150617'},
  '23646': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01149073_170414'},
  '31392': {'GT': 1, 'Acc': ' 43.33%', 'Pred': [17, 13, 0], 'edfname': '01314786_200317'},
  '07326': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [0, 2, 28], 'edfname': '00658017_180917'},
  '02330': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00343454_020414'},
  '35091': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01403151_080319'},
  '19559': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '01020558_160316'},
  '19725': {'GT': 2, 'Acc': ' 10.00%', 'Pred': [16, 11, 3], 'edfname': '01026990_241215'},
  '23657': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01149075_090414'},
  '32956': {'GT': 2, 'Acc': ' 46.67%', 'Pred': [0, 16, 14], 'edfname': '01348263_210917'},
  '12958': {'GT': 2, 'Acc': ' 90.00%', 'Pred': [1, 2, 27], 'edfname': '00803113_311016'},
  '07894': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '00671744_060718'},
  '05641': {'GT': 1, 'Acc': ' 73.33%', 'Pred': [0, 22, 8], 'edfname': '00620551_141215'},
  '17394': {'GT': 1, 'Acc': ' 76.67%', 'Pred': [0, 23, 7], 'edfname': '00954188_160118'},
  '28322': {'GT': 0, 'Acc': ' 33.33%', 'Pred': [10, 3, 17], 'edfname': '01262206_100419'},
  '11433': {'GT': 2, 'Acc': '  6.67%', 'Pred': [0, 28, 2], 'edfname': '00756447_020817'},
  '03078': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00406530_140819'},
  '17320': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00953442_060215'},
  '03303': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 7, 1], 'edfname': '00418981_060116'},
  '20323': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [3, 27, 0], 'edfname': '01055291_230517'},
  '16292': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00917762_230617'},
  '02695': {'GT': 0, 'Acc': ' 10.00%', 'Pred': [3, 2, 25], 'edfname': '00388436_200616'},
  '15554': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00895530_250716'},
  '10061': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [1, 25, 4], 'edfname': '00723110_240419'},
  '17611': {'GT': 0, 'Acc': ' 76.67%', 'Pred': [23, 0, 7], 'edfname': '00956048_290715'},
  '11443': {'GT': 2, 'Acc': ' 16.67%', 'Pred': [0, 25, 5], 'edfname': '00756447_020817'},
  '09378': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [0, 26, 4], 'edfname': '00707209_261219'},
  '07980': {'GT': 1, 'Acc': ' 66.67%', 'Pred': [0, 20, 10], 'edfname': '00673505_020419'},
  '15748': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '00899964_060115'},
  '00469': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00128526_180817'},
  '19490': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 0, 2], 'edfname': '01018913_161018'},
  '16026': {'GT': 2, 'Acc': ' 80.00%', 'Pred': [0, 6, 24], 'edfname': '00906868_270616'},
  '19328': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '01011144_070814'},
  '28757': {'GT': 0, 'Acc': ' 33.33%', 'Pred': [10, 20, 0], 'edfname': '01271175_270416'},
  '23137': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01141790_190214'},
  '04050': {'GT': 2, 'Acc': ' 83.33%', 'Pred': [5, 0, 25], 'edfname': '00489389_250319'},
  '31718': {'GT': 0, 'Acc': ' 76.67%', 'Pred': [23, 1, 6], 'edfname': '01324136_201217'},
  '07813': {'GT': 0, 'Acc': '  0.00%', 'Pred': [0, 18, 12], 'edfname': '00671212_141016'},
  '12419': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00783510_301115'},
  '07560': {'GT': 2, 'Acc': ' 40.00%', 'Pred': [0, 18, 12], 'edfname': '00665084_280219'},
  '30906': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [0, 28, 2], 'edfname': '01306354_051216'},
  '24709': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [1, 28, 1], 'edfname': '01174162_090817'},
  '35444': {'GT': 0, 'Acc': ' 43.33%', 'Pred': [13, 17, 0], 'edfname': '01410532_030419'},
  '35128': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01403371_201218'},
  '32670': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01344212_220819'},
  '06489': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00639879_061218'},
  '18098': {'GT': 1, 'Acc': ' 20.00%', 'Pred': [23, 6, 1], 'edfname': '00967966_110517'},
  '14392': {'GT': 2, 'Acc': ' 33.33%', 'Pred': [3, 17, 10], 'edfname': '00856756_281119'},
  '29948': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [5, 25, 0], 'edfname': '01293646_181016'},
  '09472': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00708411_091118'},
  '08005': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00674106_270819'},
  '01862': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00298616_290917'},
  '01307': {'GT': 1, 'Acc': ' 33.33%', 'Pred': [20, 10, 0], 'edfname': '00244987_050315'},
  '00967': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '00209996_311016'},
  '29218': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01277998_220217'},
  '11651': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00761575_090418'},
  '16766': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [2, 0, 28], 'edfname': '00939852_140214'},
  '03224': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00414635_240414'},
  '27563': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '01242982_110416'},
  '10553': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 4, 0], 'edfname': '00735258_051218'},
  '20673': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [5, 25, 0], 'edfname': '01073130_101018'},
  '01656': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [0, 28, 2], 'edfname': '00287432_091219'},
  '01393': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00257311_221018'},
  '24750': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [4, 25, 1], 'edfname': '01174220_021216'},
  '28511': {'GT': 0, 'Acc': ' 80.00%', 'Pred': [24, 6, 0], 'edfname': '01266221_090516'},
  '03529': {'GT': 1, 'Acc': ' 63.33%', 'Pred': [11, 19, 0], 'edfname': '00430974_050315'},
  '03974': {'GT': 0, 'Acc': ' 66.67%', 'Pred': [20, 10, 0], 'edfname': '00487637_190815'},
  '01312': {'GT': 1, 'Acc': ' 33.33%', 'Pred': [14, 10, 6], 'edfname': '00244987_050315'},
  '03994': {'GT': 0, 'Acc': '  6.67%', 'Pred': [2, 28, 0], 'edfname': '00487637_220715'},
  '00540': {'GT': 1, 'Acc': ' 53.33%', 'Pred': [0, 16, 14], 'edfname': '00141670_081217'},
  '30076': {'GT': 0, 'Acc': ' 16.67%', 'Pred': [5, 25, 0], 'edfname': '01296102_150217'},
  '27871': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '01251650_090819'},
  '20755': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01074533_071116'},
  '20901': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01086373_020615'},
  '06183': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [0, 27, 3], 'edfname': '00632465_150414'},
  '13941': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00840844_071118'},
  '34623': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [0, 2, 28], 'edfname': '01392274_020320'},
  '26682': {'GT': 0, 'Acc': ' 56.67%', 'Pred': [17, 12, 1], 'edfname': '01225123_070715'},
  '15407': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '00891613_210918'},
  '08161': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00675516_190919'},
  '00349': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [0, 2, 28], 'edfname': '00115426_080219'},
  '04019': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00489204_290116'},
  '15927': {'GT': 2, 'Acc': ' 73.33%', 'Pred': [0, 8, 22], 'edfname': '00906868_071216'},
  '17869': {'GT': 0, 'Acc': '  6.67%', 'Pred': [2, 28, 0], 'edfname': '00961326_010817'},
  '23461': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01147098_060515'},
  '11619': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00760780_141118'},
  '34741': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01392885_240718'},
  '12278': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00777684_300119'},
  '15430': {'GT': 1, 'Acc': '  6.67%', 'Pred': [26, 2, 2], 'edfname': '00891889_060717'},
  '19981': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01035046_020715'},
  '09386': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00707209_261219'},
  '30338': {'GT': 1, 'Acc': ' 40.00%', 'Pred': [18, 12, 0], 'edfname': '01298112_271016'},
  '26568': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01220269_141218'},
  '01695': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00287432_110518'},
  '25999': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01207886_270515'},
  '07050': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00650788_280415'},
  '20435': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01063007_171115'},
  '20284': {'GT': 2, 'Acc': ' 13.33%', 'Pred': [16, 10, 4], 'edfname': '01054491_080116'},
  '12666': {'GT': 2, 'Acc': ' 80.00%', 'Pred': [6, 0, 24], 'edfname': '00792411_261018'},
  '30898': {'GT': 1, 'Acc': '  0.00%', 'Pred': [0, 0, 30], 'edfname': '01306354_051216'},
  '06503': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00640147_140519'},
  '32312': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '01338557_190717'},
  '35102': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01403151_080319'},
  '02866': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [2, 0, 28], 'edfname': '00397252_160119'},
  '27423': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01242449_230617'},
  '04444': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00532450_220317'},
  '18869': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00988278_210915'},
  '15246': {'GT': 1, 'Acc': ' 73.33%', 'Pred': [6, 22, 2], 'edfname': '00888613_060819'},
  '17304': {'GT': 0, 'Acc': ' 80.00%', 'Pred': [24, 6, 0], 'edfname': '00952475_210518'},
  '32365': {'GT': 1, 'Acc': ' 70.00%', 'Pred': [7, 21, 2], 'edfname': '01338642_170717'},
  '10709': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [0, 2, 28], 'edfname': '00739864_070717'},
  '14737': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [0, 24, 6], 'edfname': '00873142_220319'},
  '16883': {'GT': 2, 'Acc': ' 90.00%', 'Pred': [0, 3, 27], 'edfname': '00945146_011214'},
  '17306': {'GT': 0, 'Acc': ' 16.67%', 'Pred': [5, 23, 2], 'edfname': '00952475_210518'},
  '02795': {'GT': 0, 'Acc': ' 76.67%', 'Pred': [23, 7, 0], 'edfname': '00396115_070618'},
  '25610': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [3, 26, 1], 'edfname': '01200378_230117'},
  '11405': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 0, 1], 'edfname': '00755441_151018'},
  '27530': {'GT': 0, 'Acc': ' 26.67%', 'Pred': [8, 22, 0], 'edfname': '01242981_161115'},
  '26300': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01212635_270515'},
  '08388': {'GT': 1, 'Acc': ' 26.67%', 'Pred': [22, 8, 0], 'edfname': '00678916_020718'},
  '03988': {'GT': 0, 'Acc': ' 36.67%', 'Pred': [11, 19, 0], 'edfname': '00487637_220715'},
  '19837': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01027623_260916'},
  '22067': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01134450_161019'},
  '18805': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00988278_020819'},
  '36059': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01429076_070819'},
  '03268': {'GT': 0, 'Acc': ' 43.33%', 'Pred': [13, 12, 5], 'edfname': '00417769_100117'},
  '12331': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [2, 25, 3], 'edfname': '00779511_050319'},
  '20706': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '01073130_191018'},
  '21859': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '01131959_310118'},
  '05921': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00626957_071217'},
  '03927': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00484934_090514'},
  '08505': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00684221_101016'},
  '04850': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 0, 1], 'edfname': '00606999_180817'},
  '17906': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00962086_240216'},
  '16145': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [4, 26, 0], 'edfname': '00912302_100415'},
  '32427': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '01338710_261017'},
  '01996': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00315506_250715'},
  '20953': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '01090425_290419'},
  '23493': {'GT': 0, 'Acc': ' 53.33%', 'Pred': [16, 14, 0], 'edfname': '01147098_080414'},
  '21109': {'GT': 0, 'Acc': ' 66.67%', 'Pred': [20, 10, 0], 'edfname': '01096498_270718'},
  '27027': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01235272_051115'},
  '26340': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '01215115_270715'},
  '10045': {'GT': 0, 'Acc': ' 23.33%', 'Pred': [7, 23, 0], 'edfname': '00722511_031117'},
  '06378': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '00635487_161019'},
  '09873': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00718764_270715'},
  '01766': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00293228_070918'},
  '20470': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '01063145_220915'},
  '26161': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '01209324_011217'},
  '16677': {'GT': 2, 'Acc': ' 86.67%', 'Pred': [0, 4, 26], 'edfname': '00935467_100518'},
  '31614': {'GT': 2, 'Acc': ' 70.00%', 'Pred': [1, 8, 21], 'edfname': '01321591_170417'},
  '12311': {'GT': 2, 'Acc': ' 50.00%', 'Pred': [2, 13, 15], 'edfname': '00779318_101117'},
  '28787': {'GT': 0, 'Acc': ' 80.00%', 'Pred': [24, 6, 0], 'edfname': '01271175_300516'},
  '25581': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01200248_290120'},
  '04808': {'GT': 0, 'Acc': ' 80.00%', 'Pred': [24, 6, 0], 'edfname': '00606004_110518'},
  '19347': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [6, 24, 0], 'edfname': '01011144_070814'},
  '14700': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00872871_140618'},
  '08940': {'GT': 2, 'Acc': ' 40.00%', 'Pred': [0, 18, 12], 'edfname': '00695058_191017'},
  '24426': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [0, 27, 3], 'edfname': '01163321_190718'},
  '15766': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00899964_060115'},
  '23310': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01145491_110314'},
  '14594': {'GT': 1, 'Acc': ' 63.33%', 'Pred': [4, 19, 7], 'edfname': '00867679_291117'},
  '03332': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00418981_090316'},
  '19464': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 5, 0], 'edfname': '01018538_070514'},
  '01507': {'GT': 0, 'Acc': ' 40.00%', 'Pred': [12, 17, 1], 'edfname': '00265634_301117'},
  '29466': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01282315_040716'},
  '12665': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00792411_261018'},
  '31251': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [5, 25, 0], 'edfname': '01311956_130117'},
  '00123': {'GT': 2, 'Acc': ' 80.00%', 'Pred': [0, 6, 24], 'edfname': '00048377_070916'},
  '01462': {'GT': 0, 'Acc': ' 76.67%', 'Pred': [23, 6, 1], 'edfname': '00258008_230617'},
  '17836': {'GT': 2, 'Acc': ' 16.67%', 'Pred': [4, 21, 5], 'edfname': '00960161_201014'},
  '24483': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01163965_130718'},
  '08680': {'GT': 0, 'Acc': ' 43.33%', 'Pred': [13, 7, 10], 'edfname': '00685853_200618'},
  '00352': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00115426_080219'},
  '01981': {'GT': 2, 'Acc': ' 23.33%', 'Pred': [0, 23, 7], 'edfname': '00314911_101218'},
  '14252': {'GT': 1, 'Acc': ' 66.67%', 'Pred': [10, 20, 0], 'edfname': '00851511_200818'},
  '06240': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [4, 26, 0], 'edfname': '00634366_150316'},
  '36415': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01457795_141019'},
  '14823': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00875350_140514'},
  '16174': {'GT': 1, 'Acc': ' 43.33%', 'Pred': [0, 13, 17], 'edfname': '00916265_090818'},
  '08014': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00674106_270819'},
  '03439': {'GT': 0, 'Acc': ' 26.67%', 'Pred': [8, 22, 0], 'edfname': '00429185_130917'},
  '13424': {'GT': 0, 'Acc': ' 33.33%', 'Pred': [10, 19, 1], 'edfname': '00817022_010415'},
  '09917': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '00720119_200318'},
  '25869': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01203444_240719'},
  '01336': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00248994_171016'},
  '28146': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01254424_120118'},
  '04587': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00602435_270217'},
  '21482': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01124522_170818'},
  '20359': {'GT': 0, 'Acc': ' 60.00%', 'Pred': [18, 9, 3], 'edfname': '01057134_240216'},
  '26885': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [0, 27, 3], 'edfname': '01235034_021117'},
  '00535': {'GT': 1, 'Acc': ' 63.33%', 'Pred': [0, 19, 11], 'edfname': '00141670_081217'},
  '35221': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01406657_310119'},
  '24494': {'GT': 1, 'Acc': ' 20.00%', 'Pred': [0, 6, 24], 'edfname': '01164481_220615'},
  '34803': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01395459_210819'},
  '06425': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [4, 25, 1], 'edfname': '00635487_301019'},
  '27114': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01235281_191015'},
  '00668': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '00164098_021019'},
  '23102': {'GT': 0, 'Acc': ' 33.33%', 'Pred': [10, 20, 0], 'edfname': '01141787_260315'},
  '17227': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '00951808_251116'},
  '17610': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00956048_290715'},
  '34945': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01400560_160419'},
  '36081': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01429374_230519'},
  '14975': {'GT': 1, 'Acc': ' 63.33%', 'Pred': [7, 19, 4], 'edfname': '00883719_240812'},
  '15298': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [1, 27, 2], 'edfname': '00888613_110518'},
  '08856': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [0, 2, 28], 'edfname': '00692685_200919'},
  '18815': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [0, 2, 28], 'edfname': '00988278_020819'},
  '32716': {'GT': 2, 'Acc': ' 73.33%', 'Pred': [0, 8, 22], 'edfname': '01344212_240817'},
  '27781': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '01249485_110316'},
  '21992': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '01133832_010716'},
  '14739': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00873142_220319'},
  '11013': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00745209_041018'},
  '18246': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00977111_211218'},
  '10284': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00731508_280817'},
  '30151': {'GT': 1, 'Acc': '  3.33%', 'Pred': [29, 1, 0], 'edfname': '01296344_211116'},
  '21827': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01130231_241117'},
  '23557': {'GT': 0, 'Acc': ' 70.00%', 'Pred': [21, 9, 0], 'edfname': '01147101_190314'},
  '16545': {'GT': 0, 'Acc': ' 70.00%', 'Pred': [21, 0, 9], 'edfname': '00929175_100214'},
  '23080': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01141787_190515'},
  '23419': {'GT': 0, 'Acc': ' 23.33%', 'Pred': [7, 23, 0], 'edfname': '01147097_121119'},
  '26236': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01211503_070415'},
  '05179': {'GT': 2, 'Acc': ' 76.67%', 'Pred': [2, 5, 23], 'edfname': '00613032_281215'},
  '10826': {'GT': 1, 'Acc': ' 73.33%', 'Pred': [5, 22, 3], 'edfname': '00741780_281116'},
  '21469': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01123822_271017'},
  '18296': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '00978066_170815'},
  '30317': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '01297810_151116'},
  '18423': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00980362_020616'},
  '10699': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00739864_070717'},
  '14434': {'GT': 2, 'Acc': ' 90.00%', 'Pred': [0, 3, 27], 'edfname': '00860983_271114'},
  '08529': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00684846_030717'},
  '10983': {'GT': 0, 'Acc': '  0.00%', 'Pred': [0, 30, 0], 'edfname': '00744618_290319'},
  '18639': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [0, 2, 28], 'edfname': '00986061_130716'},
  '07142': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '00654091_080618'},
  '32315': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01338557_190717'},
  '00285': {'GT': 1, 'Acc': '  6.67%', 'Pred': [28, 2, 0], 'edfname': '00088513_120717'},
  '08653': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '00685248_310315'},
  '32628': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '01342444_141118'},
  '25467': {'GT': 2, 'Acc': ' 80.00%', 'Pred': [0, 6, 24], 'edfname': '01196024_150115'},
  '32340': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '01338642_081119'},
  '33394': {'GT': 0, 'Acc': ' 56.67%', 'Pred': [17, 13, 0], 'edfname': '01356052_121217'},
  '34207': {'GT': 0, 'Acc': ' 36.67%', 'Pred': [11, 18, 1], 'edfname': '01376302_281019'},
  '32137': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01335435_280617'},
  '03763': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [6, 24, 0], 'edfname': '00474121_161118'},
  '11009': {'GT': 0, 'Acc': ' 10.00%', 'Pred': [3, 25, 2], 'edfname': '00744618_290319'},
  '27815': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01251384_041116'},
  '10815': {'GT': 0, 'Acc': ' 36.67%', 'Pred': [11, 19, 0], 'edfname': '00741571_180718'},
  '29912': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '01293646_120719'},
  '00979': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00213418_210115'},
  '05815': {'GT': 0, 'Acc': ' 20.00%', 'Pred': [6, 19, 5], 'edfname': '00626173_030118'},
  '23212': {'GT': 0, 'Acc': ' 46.67%', 'Pred': [14, 10, 6], 'edfname': '01142810_180214'},
  '25552': {'GT': 2, 'Acc': ' 23.33%', 'Pred': [1, 22, 7], 'edfname': '01200248_100215'},
  '03898': {'GT': 1, 'Acc': ' 30.00%', 'Pred': [20, 9, 1], 'edfname': '00484899_260115'},
  '17239': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '00951861_241117'},
  '16359': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00919146_120517'},
  '28527': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 5, 0], 'edfname': '01266221_280316'},
  '04535': {'GT': 2, 'Acc': ' 43.33%', 'Pred': [0, 17, 13], 'edfname': '00601765_231118'},
  '21860': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '01131959_310118'},
  '05495': {'GT': 1, 'Acc': ' 43.33%', 'Pred': [4, 13, 13], 'edfname': '00617893_231018'},
  '00906': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00203671_110518'},
  '18475': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00983533_290618'},
  '04057': {'GT': 1, 'Acc': '  0.00%', 'Pred': [30, 0, 0], 'edfname': '00492040_160516'},
  '17730': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [3, 27, 0], 'edfname': '00957862_230317'},
  '27700': {'GT': 2, 'Acc': ' 56.67%', 'Pred': [0, 13, 17], 'edfname': '01246499_301115'},
  '25523': {'GT': 0, 'Acc': ' 36.67%', 'Pred': [11, 15, 4], 'edfname': '01199993_071118'},
  '18900': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '00988618_270618'},
  '19980': {'GT': 0, 'Acc': ' 56.67%', 'Pred': [17, 9, 4], 'edfname': '01035046_020715'},
  '34484': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01382697_261218'},
  '34283': {'GT': 0, 'Acc': '  6.67%', 'Pred': [2, 28, 0], 'edfname': '01378023_300418'},
  '06864': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00649463_121219'},
  '26216': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01211467_120515'},
  '20081': {'GT': 2, 'Acc': ' 33.33%', 'Pred': [16, 4, 10], 'edfname': '01036861_090618'},
  '10069': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [1, 28, 1], 'edfname': '00723110_240419'},
  '10496': {'GT': 1, 'Acc': ' 40.00%', 'Pred': [18, 12, 0], 'edfname': '00733041_110315'},
  '12453': {'GT': 0, 'Acc': ' 63.33%', 'Pred': [19, 11, 0], 'edfname': '00783696_220414'},
  '03733': {'GT': 0, 'Acc': ' 60.00%', 'Pred': [18, 12, 0], 'edfname': '00472015_120314'},
  '01519': {'GT': 0, 'Acc': ' 36.67%', 'Pred': [11, 13, 6], 'edfname': '00265634_301117'},
  '00608': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 0, 8], 'edfname': '00160843_080219'},
  '25607': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '01200378_230117'},
  '02284': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00335823_190815'},
  '16672': {'GT': 2, 'Acc': ' 23.33%', 'Pred': [0, 23, 7], 'edfname': '00933744_170519'},
  '02142': {'GT': 2, 'Acc': ' 86.67%', 'Pred': [0, 4, 26], 'edfname': '00325929_170119'},
  '06603': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '00642740_120918'},
  '16055': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00907205_241019'},
  '26736': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01225385_060815'},
  '19000': {'GT': 0, 'Acc': ' 46.67%', 'Pred': [14, 16, 0], 'edfname': '01002918_180817'},
  '19598': {'GT': 1, 'Acc': ' 73.33%', 'Pred': [0, 22, 8], 'edfname': '01022361_010218'},
  '08761': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00688242_040919'},
  '19269': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 5, 0], 'edfname': '01010278_281116'},
  '01718': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00287432_120618'},
  '21523': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01125477_030918'},
  '02146': {'GT': 2, 'Acc': ' 70.00%', 'Pred': [1, 8, 21], 'edfname': '00325929_170119'},
  '14612': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [4, 26, 0], 'edfname': '00867679_291117'},
  '27081': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 0, 8], 'edfname': '01235273_051015'},
  '19370': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01011922_290715'},
  '26134': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '01209324_011217'},
  '15013': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00885321_170314'},
  '29776': {'GT': 1, 'Acc': ' 73.33%', 'Pred': [8, 22, 0], 'edfname': '01288483_290816'},
  '00481': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00133390_220414'},
  '07811': {'GT': 0, 'Acc': ' 33.33%', 'Pred': [10, 10, 10], 'edfname': '00671212_141016'},
  '26914': {'GT': 1, 'Acc': ' 40.00%', 'Pred': [0, 12, 18], 'edfname': '01235034_120220'},
  '11438': {'GT': 2, 'Acc': ' 73.33%', 'Pred': [0, 8, 22], 'edfname': '00756447_020817'},
  '04029': {'GT': 2, 'Acc': ' 50.00%', 'Pred': [0, 15, 15], 'edfname': '00489389_250319'},
  '06732': {'GT': 1, 'Acc': ' 20.00%', 'Pred': [24, 6, 0], 'edfname': '00645911_250315'},
  '16137': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00912302_100415'},
  '25413': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [0, 2, 28], 'edfname': '01195277_151119'},
  '26116': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01209182_270415'},
  '08118': {'GT': 2, 'Acc': ' 76.67%', 'Pred': [7, 0, 23], 'edfname': '00675516_090120'},
  '14385': {'GT': 2, 'Acc': ' 30.00%', 'Pred': [0, 21, 9], 'edfname': '00856756_281119'},
  '23030': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '01141786_190214'},
  '09088': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [0, 26, 4], 'edfname': '00698863_020419'},
  '24414': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '01161826_050916'},
  '26915': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '01235034_120220'},
  '28303': {'GT': 1, 'Acc': ' 36.67%', 'Pred': [1, 11, 18], 'edfname': '01261387_290819'},
  '07953': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00673491_141014'},
  '21576': {'GT': 2, 'Acc': ' 76.67%', 'Pred': [1, 6, 23], 'edfname': '01126210_280414'},
  '33931': {'GT': 0, 'Acc': '  0.00%', 'Pred': [0, 17, 13], 'edfname': '01368637_230218'},
  '18758': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '00987555_270717'},
  '19247': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 5, 0], 'edfname': '01010278_281116'},
  '16878': {'GT': 2, 'Acc': ' 86.67%', 'Pred': [0, 4, 26], 'edfname': '00945146_011214'},
  '12360': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00779709_280717'},
  '20214': {'GT': 2, 'Acc': ' 90.00%', 'Pred': [0, 3, 27], 'edfname': '01043928_210415'},
  '03756': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00474121_161118'},
  '13837': {'GT': 2, 'Acc': ' 70.00%', 'Pred': [3, 6, 21], 'edfname': '00839002_270218'},
  '23436': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01147097_180314'},
  '29200': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01277998_220217'},
  '03391': {'GT': 1, 'Acc': ' 70.00%', 'Pred': [0, 21, 9], 'edfname': '00427010_301118'},
  '21605': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01127053_290818'},
  '30067': {'GT': 0, 'Acc': '  3.33%', 'Pred': [1, 29, 0], 'edfname': '01296102_150217'},
  '23323': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 0, 3], 'edfname': '01145493_030414'},
  '35907': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01421529_150519'},
  '00199': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00055757_170414'},
  '15795': {'GT': 1, 'Acc': ' 23.33%', 'Pred': [23, 7, 0], 'edfname': '00899964_110414'},
  '16301': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00917762_230617'},
  '07479': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00663561_030414'},
  '15940': {'GT': 2, 'Acc': ' 86.67%', 'Pred': [0, 4, 26], 'edfname': '00906868_071216'},
  '09145': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00699594_011217'},
  '01341': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00248994_171016'},
  '02024': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00316358_070715'},
  '05562': {'GT': 1, 'Acc': ' 63.33%', 'Pred': [4, 19, 7], 'edfname': '00619387_070317'},
  '18738': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00987008_290318'},
  '02735': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 0, 1], 'edfname': '00395374_120314'},
  '25361': {'GT': 1, 'Acc': ' 23.33%', 'Pred': [23, 7, 0], 'edfname': '01193508_171214'},
  '09086': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00698863_020419'},
  '34461': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '01382697_261218'},
  '01682': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [3, 26, 1], 'edfname': '00287432_110518'},
  '16202': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [0, 26, 4], 'edfname': '00916290_270617'},
  '07887': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [0, 28, 2], 'edfname': '00671744_060718'},
  '10938': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00743464_220316'},
  '20281': {'GT': 2, 'Acc': ' 50.00%', 'Pred': [0, 15, 15], 'edfname': '01054491_080116'},
  '32627': {'GT': 1, 'Acc': ' 23.33%', 'Pred': [23, 7, 0], 'edfname': '01342444_141118'},
  '35082': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [0, 25, 5], 'edfname': '01403151_080319'},
  '33664': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [1, 0, 29], 'edfname': '01364379_230118'},
  '22712': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01139924_300315'},
  '34880': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01398060_050918'},
  '10956': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [1, 27, 2], 'edfname': '00744497_260517'},
  '12328': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '00779511_050319'},
  '15726': {'GT': 1, 'Acc': ' 70.00%', 'Pred': [2, 21, 7], 'edfname': '00899695_280918'},
  '05394': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00616379_010915'},
  '26159': {'GT': 0, 'Acc': ' 40.00%', 'Pred': [12, 16, 2], 'edfname': '01209324_011217'},
  '27275': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01240949_191015'},
  '25826': {'GT': 1, 'Acc': ' 66.67%', 'Pred': [10, 20, 0], 'edfname': '01203444_090819'},
  '17723': {'GT': 1, 'Acc': ' 56.67%', 'Pred': [13, 17, 0], 'edfname': '00957862_230317'},
  '32006': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01333917_210918'},
  '10114': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 5, 0], 'edfname': '00723144_180118'},
  '16651': {'GT': 2, 'Acc': ' 80.00%', 'Pred': [0, 6, 24], 'edfname': '00933744_170519'},
  '09517': {'GT': 0, 'Acc': ' 33.33%', 'Pred': [10, 20, 0], 'edfname': '00709574_191217'},
  '03067': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00406530_140819'},
  '03617': {'GT': 0, 'Acc': ' 56.67%', 'Pred': [17, 6, 7], 'edfname': '00456090_140417'},
  '18963': {'GT': 1, 'Acc': ' 66.67%', 'Pred': [0, 20, 10], 'edfname': '00996941_180517'},
  '09181': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00699668_200718'},
  '00669': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '00164098_021019'},
  '02734': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00395374_120314'},
  '13668': {'GT': 0, 'Acc': ' 50.00%', 'Pred': [15, 15, 0], 'edfname': '00825385_130918'},
  '25672': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01200993_060315'},
  '06828': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00649463_091115'},
  '08380': {'GT': 1, 'Acc': ' 73.33%', 'Pred': [8, 22, 0], 'edfname': '00678916_020718'},
  '16745': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '00937775_270915'},
  '08652': {'GT': 0, 'Acc': ' 80.00%', 'Pred': [24, 6, 0], 'edfname': '00685248_310315'},
  '12467': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '00784417_100315'},
  '07952': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00673491_141014'},
  '10814': {'GT': 0, 'Acc': ' 76.67%', 'Pred': [23, 6, 1], 'edfname': '00741571_180718'},
  '04541': {'GT': 2, 'Acc': ' 16.67%', 'Pred': [1, 24, 5], 'edfname': '00601765_231118'},
  '21906': {'GT': 1, 'Acc': ' 73.33%', 'Pred': [3, 22, 5], 'edfname': '01132092_171017'},
  '33130': {'GT': 1, 'Acc': ' 53.33%', 'Pred': [0, 16, 14], 'edfname': '01351393_231019'},
  '08869': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00692685_200919'},
  '18868': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00988278_210915'},
  '20274': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01053391_120618'},
  '36087': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [4, 26, 0], 'edfname': '01429374_230519'},
  '22761': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01139940_290114'},
  '06957': {'GT': 2, 'Acc': ' 80.00%', 'Pred': [6, 0, 24], 'edfname': '00649687_170314'},
  '01739': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '00290547_051018'},
  '04905': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [0, 28, 2], 'edfname': '00607068_260617'},
  '01824': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 4, 0], 'edfname': '00295596_101016'},
  '03648': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 2, 3], 'edfname': '00456090_140417'},
  '20507': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01064740_231214'},
  '00820': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '00164098_250416'},
  '05067': {'GT': 2, 'Acc': ' 66.67%', 'Pred': [0, 10, 20], 'edfname': '00609255_140417'},
  '12056': {'GT': 1, 'Acc': ' 50.00%', 'Pred': [0, 15, 15], 'edfname': '00771910_121016'},
  '23928': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '01154159_230414'},
  '22839': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01139944_040214'},
  '08353': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [0, 28, 2], 'edfname': '00678829_160217'},
  '09590': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [0, 2, 28], 'edfname': '00712852_060418'},
  '05940': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [2, 0, 28], 'edfname': '00626957_250216'},
  '34938': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01400560_160419'},
  '35633': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01415643_150119'},
  '05392': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00616379_010915'},
  '02057': {'GT': 1, 'Acc': ' 50.00%', 'Pred': [15, 15, 0], 'edfname': '00317645_311016'},
  '06382': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [1, 28, 1], 'edfname': '00635487_161019'},
  '27134': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '01235281_210915'},
  '32449': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01339759_050919'},
  '04283': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00507286_270715'},
  '26453': {'GT': 2, 'Acc': ' 63.33%', 'Pred': [0, 11, 19], 'edfname': '01216470_141118'},
  '22221': {'GT': 1, 'Acc': '  0.00%', 'Pred': [30, 0, 0], 'edfname': '01135545_160117'},
  '32509': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01339759_211119'},
  '03321': {'GT': 0, 'Acc': ' 76.67%', 'Pred': [23, 7, 0], 'edfname': '00418981_090316'},
  '34181': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01376302_151019'},
  '19324': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '01011144_070814'},
  '19577': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01022361_010218'},
  '30751': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [0, 24, 6], 'edfname': '01303479_290618'},
  '06529': {'GT': 1, 'Acc': ' 73.33%', 'Pred': [7, 22, 1], 'edfname': '00641692_170315'},
  '20841': {'GT': 0, 'Acc': ' 60.00%', 'Pred': [18, 5, 7], 'edfname': '01080162_160715'},
  '05513': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [3, 27, 0], 'edfname': '00617893_300818'},
  '12357': {'GT': 1, 'Acc': ' 73.33%', 'Pred': [8, 22, 0], 'edfname': '00779709_280717'},
  '30623': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01301982_230118'},
  '29730': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01288482_220816'},
  '08170': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00675516_231019'},
  '25996': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '01207872_281118'},
  '19820': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01027623_060317'},
  '34541': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01386864_220618'},
  '14346': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00856756_131119'},
  '22188': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01135534_230715'},
  '17874': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 1, 3], 'edfname': '00961326_010817'},
  '17701': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '00957827_230317'},
  '00293': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [3, 27, 0], 'edfname': '00088513_120717'},
  '15808': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00901507_051018'},
  '30763': {'GT': 0, 'Acc': ' 23.33%', 'Pred': [7, 19, 4], 'edfname': '01303957_051117'},
  '25613': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '01200378_230117'},
  '05915': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00626957_071217'},
  '36190': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [1, 25, 4], 'edfname': '01430323_060819'},
  '30633': {'GT': 0, 'Acc': ' 16.67%', 'Pred': [5, 25, 0], 'edfname': '01302961_060117'},
  '25591': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '01200378_230117'},
  '10092': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00723144_180118'},
  '26217': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01211467_120515'},
  '08952': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00695058_191017'},
  '17212': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00951808_251116'},
  '16200': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [0, 28, 2], 'edfname': '00916265_090818'},
  '07375': {'GT': 1, 'Acc': ' 33.33%', 'Pred': [14, 10, 6], 'edfname': '00659706_220218'},
  '21115': {'GT': 0, 'Acc': ' 80.00%', 'Pred': [24, 6, 0], 'edfname': '01096498_270718'},
  '15194': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00887757_041116'},
  '22877': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '01139946_120214'},
  '20530': {'GT': 2, 'Acc': ' 86.67%', 'Pred': [4, 0, 26], 'edfname': '01064740_231214'},
  '08528': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [3, 27, 0], 'edfname': '00684846_030717'},
  '03621': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 2, 3], 'edfname': '00456090_140417'},
  '26269': {'GT': 0, 'Acc': ' 66.67%', 'Pred': [20, 10, 0], 'edfname': '01212635_140515'},
  '35157': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01404500_080219'},
  '03129': {'GT': 1, 'Acc': ' 13.33%', 'Pred': [26, 4, 0], 'edfname': '00407878_310517'},
  '02833': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00396894_030119'},
  '00891': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00195824_201017'},
  '16590': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00931073_181018'},
  '06317': {'GT': 0, 'Acc': ' 70.00%', 'Pred': [21, 9, 0], 'edfname': '00634467_180717'},
  '05055': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00609184_070618'},
  '05387': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00616379_010915'},
  '16156': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00915077_310818'},
  '24290': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '01158725_220514'},
  '10708': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00739864_070717'},
  '18197': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '00971724_030414'},
  '12345': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00779511_050319'},
  '29198': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01277998_220217'},
  '05324': {'GT': 2, 'Acc': ' 33.33%', 'Pred': [1, 19, 10], 'edfname': '00615909_140819'},
  '28855': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01272370_271217'},
  '01611': {'GT': 1, 'Acc': ' 10.00%', 'Pred': [27, 3, 0], 'edfname': '00286931_201217'},
  '23366': {'GT': 1, 'Acc': '  6.67%', 'Pred': [28, 2, 0], 'edfname': '01146683_300419'},
  '00359': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00115426_080219'},
  '06983': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [0, 2, 28], 'edfname': '00649687_180919'},
  '10755': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [5, 25, 0], 'edfname': '00740694_110315'},
  '20539': {'GT': 2, 'Acc': '  0.00%', 'Pred': [25, 5, 0], 'edfname': '01064740_261015'},
  '29728': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01288482_220816'},
  '14333': {'GT': 0, 'Acc': ' 50.00%', 'Pred': [15, 15, 0], 'edfname': '00853892_020519'},
  '09190': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '00699668_200718'},
  '06865': {'GT': 2, 'Acc': ' 86.67%', 'Pred': [0, 4, 26], 'edfname': '00649463_121219'},
  '04264': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00507286_010915'},
  '15761': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [5, 25, 0], 'edfname': '00899964_060115'},
  '15987': {'GT': 2, 'Acc': ' 86.67%', 'Pred': [0, 4, 26], 'edfname': '00906868_231215'},
  '00675': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [0, 25, 5], 'edfname': '00164098_021019'},
  '29856': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01290676_090617'},
  '35049': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [0, 28, 2], 'edfname': '01402378_110219'},
  '30211': {'GT': 1, 'Acc': ' 66.67%', 'Pred': [2, 20, 8], 'edfname': '01296557_010819'},
  '26570': {'GT': 1, 'Acc': ' 50.00%', 'Pred': [0, 15, 15], 'edfname': '01220269_141218'},
  '19812': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01027623_060317'},
  '12464': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '00784417_100315'},
  '12845': {'GT': 0, 'Acc': ' 26.67%', 'Pred': [8, 22, 0], 'edfname': '00797248_130514'},
  '23572': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01147102_080414'},
  '15008': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00885321_170314'},
  '30663': {'GT': 1, 'Acc': ' 73.33%', 'Pred': [0, 22, 8], 'edfname': '01303196_020317'},
  '11142': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00747390_250718'},
  '26688': {'GT': 0, 'Acc': ' 53.33%', 'Pred': [16, 11, 3], 'edfname': '01225123_070715'},
  '35021': {'GT': 0, 'Acc': ' 76.67%', 'Pred': [23, 6, 1], 'edfname': '01402179_311018'},
  '31391': {'GT': 1, 'Acc': ' 43.33%', 'Pred': [17, 13, 0], 'edfname': '01314786_200317'},
  '29832': {'GT': 0, 'Acc': ' 56.67%', 'Pred': [17, 7, 6], 'edfname': '01289759_110517'},
  '32951': {'GT': 2, 'Acc': ' 90.00%', 'Pred': [0, 3, 27], 'edfname': '01348263_210917'},
  '16473': {'GT': 2, 'Acc': ' 66.67%', 'Pred': [0, 10, 20], 'edfname': '00926040_121219'},
  '01125': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [2, 27, 1], 'edfname': '00232310_261218'},
  '00028': {'GT': 0, 'Acc': ' 80.00%', 'Pred': [24, 5, 1], 'edfname': '00029426_020817'},
  '08295': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00676993_310315'},
  '07491': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [1, 0, 29], 'edfname': '00663561_030414'},
  '01994': {'GT': 0, 'Acc': ' 76.67%', 'Pred': [23, 7, 0], 'edfname': '00315506_250715'},
  '27396': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01241947_301215'},
  '02280': {'GT': 0, 'Acc': ' 70.00%', 'Pred': [21, 8, 1], 'edfname': '00335823_190815'},
  '09459': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 0, 1], 'edfname': '00708411_091118'},
  '13481': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00817092_020118'},
  '16518': {'GT': 0, 'Acc': ' 43.33%', 'Pred': [13, 17, 0], 'edfname': '00926452_230318'},
  '34246': {'GT': 0, 'Acc': ' 56.67%', 'Pred': [17, 10, 3], 'edfname': '01377415_200418'},
  '03778': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 0, 3], 'edfname': '00474445_160414'},
  '25239': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01190283_261114'},
  '11798': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00764690_050319'},
  '22621': {'GT': 2, 'Acc': ' 83.33%', 'Pred': [2, 3, 25], 'edfname': '01139924_060417'},
  '03089': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00406530_310719'},
  '00091': {'GT': 2, 'Acc': ' 63.33%', 'Pred': [0, 11, 19], 'edfname': '00048377_070819'},
  '32046': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01334570_070717'},
  '10989': {'GT': 0, 'Acc': '  0.00%', 'Pred': [0, 29, 1], 'edfname': '00744618_290319'},
  '27319': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01241945_191115'},
  '20504': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01064650_161117'},
  '01926': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00313383_180314'},
  '25354': {'GT': 1, 'Acc': ' 30.00%', 'Pred': [20, 9, 1], 'edfname': '01193508_171214'},
  '07657': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00666711_140218'},
  '27262': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01240162_121015'},
  '22180': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01135534_230715'},
  '01483': {'GT': 0, 'Acc': ' 53.33%', 'Pred': [16, 14, 0], 'edfname': '00265504_101016'},
  '03669': {'GT': 0, 'Acc': '  3.33%', 'Pred': [1, 29, 0], 'edfname': '00458897_141016'},
  '11652': {'GT': 0, 'Acc': ' 50.00%', 'Pred': [15, 15, 0], 'edfname': '00761575_090418'},
  '27085': {'GT': 0, 'Acc': ' 76.67%', 'Pred': [23, 7, 0], 'edfname': '01235273_051015'},
  '25913': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [0, 28, 2], 'edfname': '01203444_251017'},
  '01478': {'GT': 0, 'Acc': ' 50.00%', 'Pred': [15, 15, 0], 'edfname': '00265504_101016'},
  '12110': {'GT': 1, 'Acc': ' 70.00%', 'Pred': [9, 21, 0], 'edfname': '00773108_250615'},
  '08126': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00675516_090120'},
  '30140': {'GT': 1, 'Acc': '  0.00%', 'Pred': [30, 0, 0], 'edfname': '01296344_211116'},
  '30504': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01300390_251116'},
  '22392': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [0, 28, 2], 'edfname': '01137650_290819'},
  '00502': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 0, 3], 'edfname': '00138259_030518'},
  '09043': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 2, 3], 'edfname': '00697249_090414'},
  '12495': {'GT': 2, 'Acc': ' 60.00%', 'Pred': [0, 12, 18], 'edfname': '00784418_201016'},
  '05155': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00612060_221118'},
  '07948': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 0, 1], 'edfname': '00673491_141014'},
  '12455': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '00783696_220414'},
  '10290': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00731508_280817'},
  '04992': {'GT': 2, 'Acc': ' 83.33%', 'Pred': [0, 5, 25], 'edfname': '00608836_270219'},
  '12108': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [3, 27, 0], 'edfname': '00773108_250615'},
  '27481': {'GT': 0, 'Acc': ' 46.67%', 'Pred': [14, 16, 0], 'edfname': '01242661_271015'},
  '22835': {'GT': 0, 'Acc': ' 70.00%', 'Pred': [21, 9, 0], 'edfname': '01139944_040214'},
  '13461': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00817092_020118'},
  '02773': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [3, 27, 0], 'edfname': '00395714_170915'},
  '02178': {'GT': 0, 'Acc': ' 33.33%', 'Pred': [10, 16, 4], 'edfname': '00326875_050314'},
  '02892': {'GT': 2, 'Acc': ' 66.67%', 'Pred': [8, 2, 20], 'edfname': '00397252_160119'},
  '15586': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 3, 2], 'edfname': '00896386_240914'},
  '09383': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [0, 28, 2], 'edfname': '00707209_261219'},
  '15739': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [2, 25, 3], 'edfname': '00899695_280918'},
  '07284': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00657399_070515'},
  '15935': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00906868_071216'},
  '17823': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00959479_090217'},
  '20369': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01057134_310316'},
  '01973': {'GT': 2, 'Acc': ' 30.00%', 'Pred': [0, 21, 9], 'edfname': '00314911_101218'},
  '35402': {'GT': 1, 'Acc': ' 36.67%', 'Pred': [18, 11, 1], 'edfname': '01410438_241218'},
  '00652': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00162025_221215'},
  '24102': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01155731_070514'},
  '05162': {'GT': 2, 'Acc': ' 86.67%', 'Pred': [2, 2, 26], 'edfname': '00613032_281215'},
  '33684': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01364379_230118'},
  '06898': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00649687_010715'},
  '22205': {'GT': 1, 'Acc': '  6.67%', 'Pred': [28, 2, 0], 'edfname': '01135545_160117'},
  '31605': {'GT': 2, 'Acc': ' 10.00%', 'Pred': [9, 18, 3], 'edfname': '01321591_170417'},
  '22920': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01139948_290114'},
  '01402': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [4, 25, 1], 'edfname': '00257311_221018'},
  '33073': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [3, 27, 0], 'edfname': '01351393_101019'},
  '22184': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01135534_230715'},
  '14930': {'GT': 2, 'Acc': '  6.67%', 'Pred': [13, 15, 2], 'edfname': '00879020_080817'},
  '32679': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [1, 1, 28], 'edfname': '01344212_220819'},
  '14230': {'GT': 1, 'Acc': ' 73.33%', 'Pred': [8, 22, 0], 'edfname': '00851511_101018'},
  '27896': {'GT': 1, 'Acc': ' 70.00%', 'Pred': [9, 21, 0], 'edfname': '01251650_191219'},
  '11640': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '00760920_270418'},
  '09888': {'GT': 0, 'Acc': ' 40.00%', 'Pred': [12, 18, 0], 'edfname': '00720119_200318'},
  '33713': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01364379_260919'},
  '23649': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01149075_090414'},
  '25768': {'GT': 0, 'Acc': ' 80.00%', 'Pred': [24, 6, 0], 'edfname': '01201484_150618'},
  '16806': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00939957_220517'},
  '21575': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01126091_200515'},
  '08638': {'GT': 0, 'Acc': ' 43.33%', 'Pred': [13, 11, 6], 'edfname': '00685248_150414'},
  '02065': {'GT': 1, 'Acc': ' 33.33%', 'Pred': [20, 10, 0], 'edfname': '00317645_311016'},
  '22762': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01139940_290114'},
  '09355': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 0, 1], 'edfname': '00706389_270618'},
  '23233': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '01144046_260517'},
  '25040': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01182608_201014'},
  '36437': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01464624_011119'},
  '18059': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [3, 26, 1], 'edfname': '00966506_050419'},
  '09169': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00699594_011217'},
  '12053': {'GT': 1, 'Acc': ' 76.67%', 'Pred': [0, 23, 7], 'edfname': '00771910_121016'},
  '01773': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00293228_070918'},
  '01025': {'GT': 1, 'Acc': ' 40.00%', 'Pred': [18, 12, 0], 'edfname': '00220600_261114'},
  '16825': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [1, 24, 5], 'edfname': '00941070_050617'},
  '19716': {'GT': 2, 'Acc': ' 53.33%', 'Pred': [1, 13, 16], 'edfname': '01026990_241215'},
  '05828': {'GT': 0, 'Acc': ' 43.33%', 'Pred': [13, 14, 3], 'edfname': '00626173_030118'},
  '16570': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '00931073_181018'},
  '05490': {'GT': 1, 'Acc': ' 33.33%', 'Pred': [0, 10, 20], 'edfname': '00617893_231018'},
  '15059': {'GT': 1, 'Acc': ' 63.33%', 'Pred': [11, 19, 0], 'edfname': '00885959_160419'},
  '12877': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00799528_270916'},
  '04851': {'GT': 0, 'Acc': ' 63.33%', 'Pred': [19, 11, 0], 'edfname': '00606999_180817'},
  '36206': {'GT': 2, 'Acc': ' 53.33%', 'Pred': [7, 7, 16], 'edfname': '01432133_160519'},
  '08096': {'GT': 2, 'Acc': ' 70.00%', 'Pred': [0, 9, 21], 'edfname': '00675516_021019'},
  '25408': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '01195277_151119'},
  '00667': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [0, 27, 3], 'edfname': '00164098_021019'},
  '06730': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '00645911_250315'},
  '36323': {'GT': 2, 'Acc': ' 90.00%', 'Pred': [3, 0, 27], 'edfname': '01446092_160819'},
  '11467': {'GT': 1, 'Acc': ' 76.67%', 'Pred': [1, 23, 6], 'edfname': '00758349_270418'},
  '14273': {'GT': 1, 'Acc': ' 43.33%', 'Pred': [17, 13, 0], 'edfname': '00851511_200818'},
  '22714': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '01139924_300315'},
  '03502': {'GT': 2, 'Acc': ' 80.00%', 'Pred': [4, 2, 24], 'edfname': '00430821_100217'},
  '12314': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00779318_101117'},
  '24224': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01156406_120319'},
  '13633': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00824216_080416'},
  '27691': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01246499_301115'},
  '10087': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '00723110_240419'},
  '18261': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00977640_070918'},
  '07099': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [0, 26, 4], 'edfname': '00651389_281016'},
  '01562': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00278087_271114'},
  '30962': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '01306520_200117'},
  '12815': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00796775_281015'},
  '01444': {'GT': 0, 'Acc': ' 70.00%', 'Pred': [21, 8, 1], 'edfname': '00258008_230617'},
  '04585': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00602435_270217'},
  '22473': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01138298_220114'},
  '19010': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01002918_180817'},
  '00926': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00209742_081217'},
  '03922': {'GT': 0, 'Acc': ' 53.33%', 'Pred': [16, 13, 1], 'edfname': '00484934_090514'},
  '21688': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01128572_120314'},
  '13326': {'GT': 0, 'Acc': ' 26.67%', 'Pred': [8, 22, 0], 'edfname': '00813343_041218'},
  '16661': {'GT': 2, 'Acc': ' 83.33%', 'Pred': [0, 5, 25], 'edfname': '00933744_170519'},
  '17593': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 1, 3], 'edfname': '00956048_010715'},
  '23279': {'GT': 0, 'Acc': ' 30.00%', 'Pred': [9, 15, 6], 'edfname': '01145490_110314'},
  '00917': {'GT': 0, 'Acc': '  3.33%', 'Pred': [1, 28, 1], 'edfname': '00203671_110518'},
  '03607': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00450712_161118'},
  '02610': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [4, 26, 0], 'edfname': '00372136_181214'},
  '21307': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [0, 26, 4], 'edfname': '01112291_231115'},
  '07205': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00657017_101218'},
  '08564': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00684846_130417'},
  '12639': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [2, 0, 28], 'edfname': '00792411_010818'},
  '06748': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00646518_240217'},
  '10053': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '00722511_031117'},
  '26110': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01209182_250315'},
  '30173': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '01296533_281116'},
  '07956': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00673491_141014'},
  '17812': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00959479_090217'},
  '01958': {'GT': 2, 'Acc': ' 90.00%', 'Pred': [0, 3, 27], 'edfname': '00314911_101218'},
  '03543': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00433859_230519'},
  '29373': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01280476_230616'},
  '05214': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [3, 27, 0], 'edfname': '00613313_230518'},
  '23694': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01149084_090414'},
  '18370': {'GT': 1, 'Acc': ' 40.00%', 'Pred': [3, 12, 15], 'edfname': '00978569_251018'},
  '25196': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01189271_031215'},
  '06952': {'GT': 2, 'Acc': ' 83.33%', 'Pred': [5, 0, 25], 'edfname': '00649687_170314'},
  '06187': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00633197_091215'},
  '03765': {'GT': 1, 'Acc': ' 36.67%', 'Pred': [16, 11, 3], 'edfname': '00474121_161118'},
  '03014': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 4, 0], 'edfname': '00405960_240414'},
  '18550': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [0, 28, 2], 'edfname': '00984999_070617'},
  '18712': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00986061_270116'},
  '16127': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00912302_100415'},
  '08372': {'GT': 1, 'Acc': ' 63.33%', 'Pred': [9, 19, 2], 'edfname': '00678916_020718'},
  '06882': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00649687_010715'},
  '33361': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01355599_101117'},
  '21574': {'GT': 0, 'Acc': ' 26.67%', 'Pred': [8, 0, 22], 'edfname': '01126091_200515'},
  '28468': {'GT': 2, 'Acc': ' 90.00%', 'Pred': [2, 1, 27], 'edfname': '01264980_150416'},
  '35629': {'GT': 2, 'Acc': ' 43.33%', 'Pred': [0, 17, 13], 'edfname': '01415643_150119'},
  '28777': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01271175_300516'},
  '02887': {'GT': 2, 'Acc': ' 40.00%', 'Pred': [0, 18, 12], 'edfname': '00397252_160119'},
  '00081': {'GT': 2, 'Acc': ' 53.33%', 'Pred': [0, 14, 16], 'edfname': '00048377_070819'},
  '12371': {'GT': 1, 'Acc': ' 30.00%', 'Pred': [5, 9, 16], 'edfname': '00779709_280717'},
  '29865': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 0, 1], 'edfname': '01290676_090617'},
  '13986': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 4, 0], 'edfname': '00840844_250119'},
  '26446': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [6, 24, 0], 'edfname': '01216443_240518'},
  '36097': {'GT': 1, 'Acc': ' 73.33%', 'Pred': [8, 22, 0], 'edfname': '01429374_230519'},
  '14178': {'GT': 1, 'Acc': ' 50.00%', 'Pred': [15, 15, 0], 'edfname': '00850889_290817'},
  '23017': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01141785_130214'},
  '26664': {'GT': 0, 'Acc': ' 43.33%', 'Pred': [13, 17, 0], 'edfname': '01225123_050815'},
  '06105': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00631178_110517'},
  '23336': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01145494_030414'},
  '17516': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00955171_310816'},
  '02050': {'GT': 1, 'Acc': ' 46.67%', 'Pred': [16, 14, 0], 'edfname': '00317645_311016'},
  '35874': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01420059_050319'},
  '17712': {'GT': 0, 'Acc': ' 33.33%', 'Pred': [10, 20, 0], 'edfname': '00957827_230317'},
  '33096': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01351393_111119'},
  '21416': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [3, 27, 0], 'edfname': '01116389_271118'},
  '14165': {'GT': 2, 'Acc': ' 80.00%', 'Pred': [0, 6, 24], 'edfname': '00848071_121018'},
  '01753': {'GT': 1, 'Acc': ' 76.67%', 'Pred': [6, 23, 1], 'edfname': '00293228_070918'},
  '35427': {'GT': 0, 'Acc': ' 76.67%', 'Pred': [23, 7, 0], 'edfname': '01410532_030419'},
  '22993': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '01141783'},
  '35103': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '01403151_080319'},
  '25583': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01200248_290120'},
  '30482': {'GT': 1, 'Acc': ' 40.00%', 'Pred': [18, 12, 0], 'edfname': '01299709_090419'},
  '16451': {'GT': 2, 'Acc': ' 76.67%', 'Pred': [6, 1, 23], 'edfname': '00926040_061115'},
  '18697': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00986061_270116'},
  '02580': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00369252_131216'},
  '27661': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '01243336_240117'},
  '08883': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00693392_210617'},
  '30044': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01295899_041016'},
  '00314': {'GT': 0, 'Acc': ' 70.00%', 'Pred': [21, 0, 9], 'edfname': '00110793_210917'},
  '16050': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00907205_241019'},
  '02707': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00389465_040517'},
  '13448': {'GT': 0, 'Acc': ' 66.67%', 'Pred': [20, 0, 10], 'edfname': '00817054_100918'},
  '34046': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [1, 1, 28], 'edfname': '01371418_090318'},
  '21592': {'GT': 2, 'Acc': ' 66.67%', 'Pred': [7, 3, 20], 'edfname': '01126210_280414'},
  '10021': {'GT': 1, 'Acc': ' 56.67%', 'Pred': [0, 17, 13], 'edfname': '00722305_261018'},
  '20340': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01056827_240818'},
  '36388': {'GT': 2, 'Acc': ' 10.00%', 'Pred': [0, 27, 3], 'edfname': '01453571_270819'},
  '34237': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01377415_200418'},
  '11440': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00756447_020817'},
  '14898': {'GT': 1, 'Acc': '  0.00%', 'Pred': [30, 0, 0], 'edfname': '00877170_301214'},
  '31386': {'GT': 1, 'Acc': ' 13.33%', 'Pred': [26, 4, 0], 'edfname': '01314786_200317'},
  '07378': {'GT': 1, 'Acc': ' 66.67%', 'Pred': [0, 20, 10], 'edfname': '00659706_220218'},
  '34281': {'GT': 0, 'Acc': ' 36.67%', 'Pred': [11, 19, 0], 'edfname': '01378023_300418'},
  '15603': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 0, 3], 'edfname': '00896386_240914'},
  '03636': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00456090_140417'},
  '11776': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00763580_281118'},
  '13150': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '00809059_030715'},
  '26246': {'GT': 0, 'Acc': ' 53.33%', 'Pred': [16, 8, 6], 'edfname': '01211503_120515'},
  '07835': {'GT': 0, 'Acc': ' 10.00%', 'Pred': [3, 26, 1], 'edfname': '00671212_160819'},
  '35763': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [0, 28, 2], 'edfname': '01418070_200819'},
  '34466': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 4, 0], 'edfname': '01382697_261218'},
  '01910': {'GT': 1, 'Acc': ' 73.33%', 'Pred': [7, 22, 1], 'edfname': '00310451_130214'},
  '27340': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01241945_231215'},
  '06521': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '00641692_170315'},
  '05755': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00623681_070415'},
  '27271': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01240949_191015'},
  '21457': {'GT': 1, 'Acc': ' 63.33%', 'Pred': [5, 19, 6], 'edfname': '01123822_271017'},
  '12387': {'GT': 1, 'Acc': ' 50.00%', 'Pred': [0, 15, 15], 'edfname': '00781425_130718'},
  '18740': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00987008_290318'},
  '31275': {'GT': 1, 'Acc': ' 26.67%', 'Pred': [22, 8, 0], 'edfname': '01311956_130117'},
  '08866': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00692685_200919'},
  '20261': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01053391_120618'},
  '17229': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [0, 27, 3], 'edfname': '00951808_251116'},
  '11109': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00747390_130918'},
  '18343': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [0, 27, 3], 'edfname': '00978569_110119'},
  '18042': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00963680_280519'},
  '27974': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01252396_141216'},
  '31871': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01329562_150518'},
  '16739': {'GT': 1, 'Acc': ' 23.33%', 'Pred': [0, 7, 23], 'edfname': '00937775_270915'},
  '06979': {'GT': 2, 'Acc': ' 56.67%', 'Pred': [0, 13, 17], 'edfname': '00649687_180919'},
  '08074': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00675186_200514'},
  '29110': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01276868_020616'},
  '27594': {'GT': 1, 'Acc': ' 10.00%', 'Pred': [27, 3, 0], 'edfname': '01242984_211215'},
  '28536': {'GT': 0, 'Acc': ' 76.67%', 'Pred': [23, 7, 0], 'edfname': '01266696_110516'},
  '22959': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01139951_060214'},
  '14764': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00875320_070415'},
  '29932': {'GT': 1, 'Acc': ' 63.33%', 'Pred': [8, 19, 3], 'edfname': '01293646_181016'},
  '04840': {'GT': 0, 'Acc': ' 33.33%', 'Pred': [10, 20, 0], 'edfname': '00606999_180817'},
  '22618': {'GT': 1, 'Acc': ' 60.00%', 'Pred': [0, 18, 12], 'edfname': '01139894_140214'},
  '12945': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00803113_261115'},
  '12431': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00783510_301115'},
  '03631': {'GT': 0, 'Acc': ' 63.33%', 'Pred': [19, 11, 0], 'edfname': '00456090_140417'},
  '32479': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01339759_071119'},
  '05424': {'GT': 1, 'Acc': '  6.67%', 'Pred': [28, 2, 0], 'edfname': '00617283_230318'},
  '20002': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01035046_300715'},
  '35855': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01419871_080219'},
  '34661': {'GT': 0, 'Acc': ' 60.00%', 'Pred': [18, 11, 1], 'edfname': '01392606_260718'},
  '08781': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 4, 0], 'edfname': '00689609_270514'},
  '01132': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00232310_261218'},
  '24731': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [0, 28, 2], 'edfname': '01174162_090817'},
  '26275': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01212635_140515'},
  '20008': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '01035046_300715'},
  '14558': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [0, 27, 3], 'edfname': '00865039_170816'},
  '00179': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00050941_130116'},
  '04156': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00502785_121219'},
  '22825': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01139942_060214'},
  '36311': {'GT': 2, 'Acc': ' 63.33%', 'Pred': [1, 10, 19], 'edfname': '01446092_160819'},
  '22649': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01139924_140717'},
  '05766': {'GT': 2, 'Acc': ' 80.00%', 'Pred': [0, 6, 24], 'edfname': '00623681_070415'},
  '24217': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '01156406_120319'},
  '09918': {'GT': 0, 'Acc': ' 50.00%', 'Pred': [15, 14, 1], 'edfname': '00720119_200318'},
  '16695': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00935598_301018'},
  '06866': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00649463_121219'},
  '33160': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01351393_261017'},
  '29084': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '01276737_300616'},
  '24453': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [2, 24, 4], 'edfname': '01163321_210918'},
  '14476': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00861148_300119'},
  '07274': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 0, 2], 'edfname': '00657399_020415'},
  '29083': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01276737_300616'},
  '26668': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '01225123_050815'},
  '26129': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01209182_270415'},
  '28594': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01266829_070416'},
  '12426': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00783510_301115'},
  '19574': {'GT': 1, 'Acc': ' 76.67%', 'Pred': [6, 23, 1], 'edfname': '01022361_010218'},
  '21844': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '01131959_310118'},
  '25303': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 3, 1], 'edfname': '01191562_091214'},
  '18782': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 0, 1], 'edfname': '00987670_270514'},
  '22996': {'GT': 0, 'Acc': ' 33.33%', 'Pred': [10, 1, 19], 'edfname': '01141783'},
  '30788': {'GT': 0, 'Acc': ' 16.67%', 'Pred': [5, 23, 2], 'edfname': '01303957_051117'},
  '31016': {'GT': 0, 'Acc': ' 46.67%', 'Pred': [14, 16, 0], 'edfname': '01306737_151216'},
  '26438': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [3, 25, 2], 'edfname': '01216443_240518'},
  '03772': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 0, 2], 'edfname': '00474445_160414'},
  '07772': {'GT': 0, 'Acc': ' 63.33%', 'Pred': [19, 11, 0], 'edfname': '00671212_020819'},
  '27104': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01235281_191015'},
  '19775': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [0, 2, 28], 'edfname': '01027623_040416'},
  '34502': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [1, 27, 2], 'edfname': '01382733_150518'},
  '22803': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 5, 0], 'edfname': '01139941_050214'},
  '00320': {'GT': 0, 'Acc': ' 43.33%', 'Pred': [13, 17, 0], 'edfname': '00110793_210917'},
  '21610': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '01127053_290818'},
  '29760': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01288483_290816'},
  '30375': {'GT': 1, 'Acc': ' 60.00%', 'Pred': [0, 18, 12], 'edfname': '01298381_281016'},
  '23106': {'GT': 0, 'Acc': ' 63.33%', 'Pred': [19, 11, 0], 'edfname': '01141787_260315'},
  '05410': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 4, 0], 'edfname': '00616379_270715'},
  '22941': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01139951_060214'},
  '14647': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '00869351_160217'},
  '27676': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01246499_301115'},
  '27098': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01235281_191015'},
  '22246': {'GT': 1, 'Acc': ' 33.33%', 'Pred': [20, 10, 0], 'edfname': '01135545_200815'},
  '06309': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '00634467_180717'},
  '28181': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01256391_310316'},
  '00648': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00162025_221215'},
  '31428': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [3, 27, 0], 'edfname': '01314894_190517'},
  '06306': {'GT': 1, 'Acc': ' 40.00%', 'Pred': [0, 12, 18], 'edfname': '00634366_311019'},
  '23260': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '01145402_161117'},
  '31340': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01314783_100417'},
  '27255': {'GT': 0, 'Acc': ' 80.00%', 'Pred': [24, 6, 0], 'edfname': '01240162_121015'},
  '14910': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00879020_080817'},
  '33513': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01358607_280918'},
  '30336': {'GT': 1, 'Acc': ' 10.00%', 'Pred': [22, 3, 5], 'edfname': '01298112_271016'},
  '21600': {'GT': 0, 'Acc': ' 63.33%', 'Pred': [19, 11, 0], 'edfname': '01127053_290818'},
  '32470': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01339759_071119'},
  '23467': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01147098_060515'},
  '36308': {'GT': 2, 'Acc': ' 16.67%', 'Pred': [0, 25, 5], 'edfname': '01446092_160819'},
  '25537': {'GT': 2, 'Acc': ' 63.33%', 'Pred': [0, 11, 19], 'edfname': '01200248_100215'},
  '06606': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '00642740_120918'},
  '28112': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01254379_210116'},
  '07701': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00666711_150316'},
  '12850': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 5, 0], 'edfname': '00797248_130514'},
  '35325': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01409497_111219'},
  '06374': {'GT': 1, 'Acc': ' 76.67%', 'Pred': [0, 23, 7], 'edfname': '00635487_161019'},
  '25883': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01203444_240719'},
  '28555': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01266696_110516'},
  '00760': {'GT': 1, 'Acc': ' 70.00%', 'Pred': [0, 21, 9], 'edfname': '00164098_180919'},
  '10004': {'GT': 1, 'Acc': ' 76.67%', 'Pred': [0, 23, 7], 'edfname': '00722305_261018'},
  '00947': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00209742_081217'},
  '26392': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01215547_290415'},
  '04334': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00521264_170815'},
  '15299': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [1, 28, 1], 'edfname': '00888613_110518'},
  '13652': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00824216_110516'},
  '16501': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00926040_201119'},
  '05950': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00626957_250216'},
  '23321': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 1, 2], 'edfname': '01145493_030414'},
  '21871': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01131959_310118'},
  '06253': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00634366_241019'},
  '19380': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01011922_290715'},
  '01359': {'GT': 0, 'Acc': ' 16.67%', 'Pred': [5, 15, 10], 'edfname': '00250122_100519'},
  '16660': {'GT': 2, 'Acc': ' 63.33%', 'Pred': [0, 11, 19], 'edfname': '00933744_170519'},
  '01542': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00270630_050314'},
  '00435': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00127458_150618'},
  '02935': {'GT': 0, 'Acc': ' 70.00%', 'Pred': [21, 9, 0], 'edfname': '00399896_180414'},
  '06078': {'GT': 1, 'Acc': ' 56.67%', 'Pred': [11, 17, 2], 'edfname': '00628699_210519'},
  '10684': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00739162_141117'},
  '13616': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00824216_080416'},
  '08216': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [1, 0, 29], 'edfname': '00675516_270214'},
  '14803': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00875320_220414'},
  '30650': {'GT': 0, 'Acc': '  0.00%', 'Pred': [0, 30, 0], 'edfname': '01302961_060117'},
  '05173': {'GT': 2, 'Acc': ' 83.33%', 'Pred': [1, 4, 25], 'edfname': '00613032_281215'},
  '23469': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01147098_060515'},
  '11410': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 0, 1], 'edfname': '00755441_151018'},
  '00818': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '00164098_250416'},
  '23584': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01147102_080414'},
  '03320': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00418981_090316'},
  '05072': {'GT': 2, 'Acc': ' 86.67%', 'Pred': [0, 4, 26], 'edfname': '00609255_140417'},
  '27540': {'GT': 0, 'Acc': ' 60.00%', 'Pred': [18, 12, 0], 'edfname': '01242981_161115'},
  '24377': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 7, 1], 'edfname': '01159816_310315'},
  '25442': {'GT': 2, 'Acc': ' 80.00%', 'Pred': [0, 6, 24], 'edfname': '01196024_150115'},
  '20226': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01043928_210415'},
  '10193': {'GT': 1, 'Acc': ' 63.33%', 'Pred': [11, 19, 0], 'edfname': '00725683_091115'},
  '22161': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01135534_200815'},
  '23501': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01147098_310315'},
  '28492': {'GT': 2, 'Acc': ' 83.33%', 'Pred': [5, 0, 25], 'edfname': '01264980_200516'},
  '23474': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '01147098_060515'},
  '07930': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '00672867_031116'},
  '33426': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01357637_241117'},
  '15783': {'GT': 1, 'Acc': ' 73.33%', 'Pred': [7, 22, 1], 'edfname': '00899964_110414'},
  '34786': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01393920_091118'},
  '08371': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '00678916_020718'},
  '32561': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '01339759_310717'},
  '26188': {'GT': 2, 'Acc': ' 83.33%', 'Pred': [0, 5, 25], 'edfname': '01210535_260515'},
  '11041': {'GT': 2, 'Acc': ' 16.67%', 'Pred': [14, 11, 5], 'edfname': '00745209_220916'},
  '30085': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01296102_161116'},
  '07907': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00671744_060718'},
  '26094': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01209181_270415'},
  '02195': {'GT': 0, 'Acc': ' 30.00%', 'Pred': [9, 10, 11], 'edfname': '00329526_161118'},
  '03802': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00480292_130616'},
  '27887': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '01251650_090819'},
  '30181': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 4, 0], 'edfname': '01296533_281116'},
  '20269': {'GT': 2, 'Acc': ' 40.00%', 'Pred': [15, 3, 12], 'edfname': '01053391_120618'},
  '20528': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01064740_231214'},
  '19013': {'GT': 0, 'Acc': ' 50.00%', 'Pred': [15, 8, 7], 'edfname': '01002918_180817'},
  '22582': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01138910_270114'},
  '12267': {'GT': 1, 'Acc': ' 26.67%', 'Pred': [21, 8, 1], 'edfname': '00777684_300119'},
  '25045': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01182608_201014'},
  '18763': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [0, 28, 2], 'edfname': '00987555_270717'},
  '08693': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [0, 27, 3], 'edfname': '00687089_011216'},
  '11628': {'GT': 0, 'Acc': ' 63.33%', 'Pred': [19, 11, 0], 'edfname': '00760780_141118'},
  '36341': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01450766_200919'},
  '28423': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01264839_070416'},
  '15066': {'GT': 1, 'Acc': ' 66.67%', 'Pred': [2, 20, 8], 'edfname': '00885959_160419'},
  '22243': {'GT': 1, 'Acc': '  0.00%', 'Pred': [30, 0, 0], 'edfname': '01135545_200815'},
  '09840': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00717883_260814'},
  '16008': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00906868_270616'},
  '00154': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00048377_250719'},
  '21510': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01125477_030918'},
  '01946': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00313383_180314'},
  '26692': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01225382_060815'},
  '08589': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '00685191_100419'},
  '13417': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00817022_010415'},
  '02603': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00372136_181214'},
  '20637': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '01071934_020415'},
  '14648': {'GT': 1, 'Acc': ' 33.33%', 'Pred': [20, 10, 0], 'edfname': '00869351_160217'},
  '18983': {'GT': 2, 'Acc': ' 10.00%', 'Pred': [9, 18, 3], 'edfname': '01000945_250615'},
  '20848': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '01080162_160715'},
  '31066': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01307883_100217'},
  '20073': {'GT': 2, 'Acc': ' 10.00%', 'Pred': [0, 27, 3], 'edfname': '01036861_090618'},
  '01938': {'GT': 0, 'Acc': ' 63.33%', 'Pred': [19, 11, 0], 'edfname': '00313383_180314'},
  '13038': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00803920_221217'},
  '02366': {'GT': 1, 'Acc': ' 70.00%', 'Pred': [9, 21, 0], 'edfname': '00344923_021116'},
  '13387': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00817008_030414'},
  '28674': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [4, 24, 2], 'edfname': '01269786_071019'},
  '34150': {'GT': 0, 'Acc': ' 63.33%', 'Pred': [19, 11, 0], 'edfname': '01376302_060718'},
  '14335': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00856756_131119'},
  '21879': {'GT': 0, 'Acc': ' 80.00%', 'Pred': [24, 6, 0], 'edfname': '01132052_230518'},
  '21948': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '01132092_271218'},
  '05985': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [0, 28, 2], 'edfname': '00626969_221117'},
  '21567': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01126091_200515'},
  '02747': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '00395374_120314'},
  '07222': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00657017_101218'},
  '26297': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 3, 1], 'edfname': '01212635_270515'},
  '29274': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '01279168_171219'},
  '24298': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 5, 0], 'edfname': '01159187_131118'},
  '08487': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00684221_101016'},
  '30135': {'GT': 1, 'Acc': ' 10.00%', 'Pred': [27, 3, 0], 'edfname': '01296342_141116'},
  '09147': {'GT': 2, 'Acc': ' 66.67%', 'Pred': [0, 10, 20], 'edfname': '00699594_011217'},
  '14818': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00875350_140514'},
  '19793': {'GT': 2, 'Acc': ' 86.67%', 'Pred': [0, 4, 26], 'edfname': '01027623_040416'},
  '23518': {'GT': 0, 'Acc': ' 43.33%', 'Pred': [13, 17, 0], 'edfname': '01147099_130314'},
  '00122': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00048377_070916'},
  '03032': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00406530_120419'},
  '08534': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00684846_030717'},
  '10274': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00729370_190214'},
  '19427': {'GT': 0, 'Acc': ' 63.33%', 'Pred': [19, 11, 0], 'edfname': '01017337_140219'},
  '03123': {'GT': 1, 'Acc': ' 53.33%', 'Pred': [14, 16, 0], 'edfname': '00407878_310517'},
  '14271': {'GT': 1, 'Acc': ' 73.33%', 'Pred': [8, 22, 0], 'edfname': '00851511_200818'},
  '07397': {'GT': 1, 'Acc': ' 30.00%', 'Pred': [21, 9, 0], 'edfname': '00660468_191214'},
  '03816': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00480292_130616'},
  '05622': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [0, 25, 5], 'edfname': '00620551_141215'},
  '25917': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '01203444_251017'},
  '35073': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01402378_110219'},
  '12957': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00803113_311016'},
  '30311': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [0, 2, 28], 'edfname': '01297810_151116'},
  '00910': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '00203671_110518'},
  '32078': {'GT': 2, 'Acc': ' 60.00%', 'Pred': [0, 12, 18], 'edfname': '01334756_191217'},
  '10186': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [5, 25, 0], 'edfname': '00725683_091115'},
  '07161': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00654671_151018'},
  '16217': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00916290_270617'},
  '23982': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 2, 1], 'edfname': '01154834_011019'},
  '01964': {'GT': 2, 'Acc': ' 90.00%', 'Pred': [0, 3, 27], 'edfname': '00314911_101218'},
  '12505': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00784418_201016'},
  '22706': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '01139924_300315'},
  '29709': {'GT': 0, 'Acc': ' 20.00%', 'Pred': [6, 1, 23], 'edfname': '01287702_071218'},
  '23498': {'GT': 0, 'Acc': ' 76.67%', 'Pred': [23, 7, 0], 'edfname': '01147098_310315'},
  '16894': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00945146_011214'},
  '16094': {'GT': 1, 'Acc': ' 56.67%', 'Pred': [13, 17, 0], 'edfname': '00907971_030217'},
  '27499': {'GT': 0, 'Acc': ' 80.00%', 'Pred': [24, 6, 0], 'edfname': '01242663_011215'},
  '29539': {'GT': 1, 'Acc': ' 53.33%', 'Pred': [0, 16, 14], 'edfname': '01284214_160816'},
  '05790': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00625454_251116'},
  '01318': {'GT': 1, 'Acc': ' 50.00%', 'Pred': [14, 15, 1], 'edfname': '00244987_050315'},
  '24011': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 1, 3], 'edfname': '01154834_180417'},
  '20315': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01055291_230517'},
  '17728': {'GT': 1, 'Acc': ' 73.33%', 'Pred': [8, 22, 0], 'edfname': '00957862_230317'},
  '16035': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00906868_270616'},
  '12286': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '00777684_300119'},
  '21874': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01132052_230518'},
  '06359': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 5, 0], 'edfname': '00635370_170220'},
  '00873': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00195824_201017'},
  '16552': {'GT': 0, 'Acc': ' 50.00%', 'Pred': [15, 1, 14], 'edfname': '00929175_100214'},
  '23316': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01145493_030414'},
  '22381': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [0, 26, 4], 'edfname': '01137650_290819'},
  '05772': {'GT': 2, 'Acc': ' 70.00%', 'Pred': [4, 5, 21], 'edfname': '00623681_070415'},
  '06216': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00633563_061015'},
  '01785': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00293228_120718'},
  '27320': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 5, 0], 'edfname': '01241945_191115'},
  '20882': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01081922_071215'},
  '26106': {'GT': 0, 'Acc': ' 63.33%', 'Pred': [19, 3, 8], 'edfname': '01209182_250315'},
  '00049': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '00029426_020817'},
  '23070': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01141787_181114'},
  '22728': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '01139924_300315'},
  '34682': {'GT': 0, 'Acc': ' 50.00%', 'Pred': [15, 15, 0], 'edfname': '01392607_260718'},
  '31415': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '01314894_190517'},
  '27528': {'GT': 0, 'Acc': ' 70.00%', 'Pred': [21, 9, 0], 'edfname': '01242981_161115'},
  '19285': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01010605_160514'},
  '18365': {'GT': 1, 'Acc': ' 33.33%', 'Pred': [16, 10, 4], 'edfname': '00978569_251018'},
  '02830': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [0, 28, 2], 'edfname': '00396894_030119'},
  '23565': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '01147101_190314'},
  '00480': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00133390_220414'},
  '02622': {'GT': 0, 'Acc': ' 63.33%', 'Pred': [19, 9, 2], 'edfname': '00378481_210617'},
  '09871': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00718764_270715'},
  '33091': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01351393_111119'},
  '06498': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00640147_140519'},
  '01831': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '00295596_101016'},
  '14934': {'GT': 2, 'Acc': ' 63.33%', 'Pred': [0, 11, 19], 'edfname': '00879020_080817'},
  '18949': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [0, 28, 2], 'edfname': '00996941_180517'},
  '20937': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01086373_290415'},
  '34189': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01376302_151019'},
  '20134': {'GT': 1, 'Acc': ' 66.67%', 'Pred': [0, 20, 10], 'edfname': '01041208_020818'},
  '06611': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00642740_120918'},
  '31050': {'GT': 1, 'Acc': ' 76.67%', 'Pred': [7, 23, 0], 'edfname': '01307080_060117'},
  '12901': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00803113_260516'},
  '30025': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '01295899_041016'},
  '07281': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00657399_020415'},
  '26599': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '01222061_271017'},
  '13000': {'GT': 1, 'Acc': ' 56.67%', 'Pred': [1, 17, 12], 'edfname': '00803780_071218'},
  '04260': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00507286_010915'},
  '32484': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01339759_071119'},
  '32387': {'GT': 1, 'Acc': ' 50.00%', 'Pred': [14, 15, 1], 'edfname': '01338642_170717'},
  '35956': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '01422773_010419'},
  '06304': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [0, 25, 5], 'edfname': '00634366_311019'},
  '07598': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '00665821_260815'},
  '33056': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01351393_101019'},
  '10698': {'GT': 2, 'Acc': ' 70.00%', 'Pred': [0, 9, 21], 'edfname': '00739864_070717'},
  '03608': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '00450712_161118'},
  '03622': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00456090_140417'},
  '30914': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [4, 26, 0], 'edfname': '01306496_130117'},
  '09670': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00714598_150917'},
  '05904': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [0, 2, 28], 'edfname': '00626957_071217'},
  '06376': {'GT': 1, 'Acc': ' 76.67%', 'Pred': [7, 23, 0], 'edfname': '00635487_161019'},
  '32494': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01339759_071119'},
  '30343': {'GT': 1, 'Acc': ' 26.67%', 'Pred': [22, 8, 0], 'edfname': '01298112_271016'},
  '12940': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00803113_261115'},
  '13683': {'GT': 0, 'Acc': ' 10.00%', 'Pred': [3, 27, 0], 'edfname': '00825385_130918'},
  '11793': {'GT': 2, 'Acc': ' 86.67%', 'Pred': [0, 4, 26], 'edfname': '00764690_050319'},
  '00810': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [4, 26, 0], 'edfname': '00164098_230317'},
  '23429': {'GT': 0, 'Acc': '  6.67%', 'Pred': [2, 26, 2], 'edfname': '01147097_121119'},
  '16755': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00939852_140214'},
  '35417': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01410438_241218'},
  '16357': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '00919146_120517'},
  '13959': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00840844_250119'},
  '00441': {'GT': 1, 'Acc': '  6.67%', 'Pred': [28, 2, 0], 'edfname': '00127458_150618'},
  '29882': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [1, 0, 29], 'edfname': '01291791_100217'},
  '15211': {'GT': 0, 'Acc': '  3.33%', 'Pred': [1, 29, 0], 'edfname': '00887981_210617'},
  '11591': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00760504_080618'},
  '34646': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '01392606_260718'},
  '30514': {'GT': 1, 'Acc': ' 16.67%', 'Pred': [25, 5, 0], 'edfname': '01300390_251116'},
  '12242': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00777577_120314'},
  '17041': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00949701_180219'},
  '30509': {'GT': 1, 'Acc': ' 46.67%', 'Pred': [10, 14, 6], 'edfname': '01300390_251116'},
  '22306': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01136368_160114'},
  '25580': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01200248_290120'},
  '05411': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '00616379_270715'},
  '25655': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 5, 0], 'edfname': '01200993_060315'},
  '11166': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00752045_260419'},
  '27444': {'GT': 1, 'Acc': ' 33.33%', 'Pred': [0, 10, 20], 'edfname': '01242449_230617'},
  '33872': {'GT': 2, 'Acc': ' 86.67%', 'Pred': [1, 3, 26], 'edfname': '01367495_310118'},
  '26447': {'GT': 1, 'Acc': ' 26.67%', 'Pred': [22, 8, 0], 'edfname': '01216443_240518'},
  '00322': {'GT': 0, 'Acc': ' 66.67%', 'Pred': [20, 10, 0], 'edfname': '00110793_210917'},
  '05771': {'GT': 2, 'Acc': ' 83.33%', 'Pred': [1, 4, 25], 'edfname': '00623681_070415'},
  '02506': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 7, 1], 'edfname': '00356300_100414'},
  '27842': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '01251650_060116'},
  '00043': {'GT': 0, 'Acc': ' 26.67%', 'Pred': [8, 17, 5], 'edfname': '00029426_020817'},
  '14902': {'GT': 1, 'Acc': ' 76.67%', 'Pred': [3, 23, 4], 'edfname': '00877170_301214'},
  '14606': {'GT': 1, 'Acc': ' 66.67%', 'Pred': [10, 20, 0], 'edfname': '00867679_291117'},
  '24093': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01155731_070514'},
  '02176': {'GT': 0, 'Acc': ' 16.67%', 'Pred': [5, 3, 22], 'edfname': '00326875_050314'},
  '34852': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '01397646_281019'},
  '19550': {'GT': 2, 'Acc': ' 66.67%', 'Pred': [0, 10, 20], 'edfname': '01020558_160316'},
  '06119': {'GT': 2, 'Acc': ' 10.00%', 'Pred': [0, 27, 3], 'edfname': '00631526_070716'},
  '20354': {'GT': 0, 'Acc': ' 80.00%', 'Pred': [24, 6, 0], 'edfname': '01057134_240216'},
  '17259': {'GT': 0, 'Acc': ' 63.33%', 'Pred': [19, 2, 9], 'edfname': '00951861_241117'},
  '25198': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01189271_031215'},
  '01429': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00257387_220115'},
  '22529': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01138302_160114'},
  '18478': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '00983533_290618'},
  '24768': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '01174220_021216'},
  '05842': {'GT': 0, 'Acc': ' 16.67%', 'Pred': [5, 25, 0], 'edfname': '00626173_030118'},
  '05227': {'GT': 1, 'Acc': ' 33.33%', 'Pred': [20, 10, 0], 'edfname': '00613313_230518'},
  '34199': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01376302_151019'},
  '21542': {'GT': 0, 'Acc': ' 46.67%', 'Pred': [14, 16, 0], 'edfname': '01126091_140415'},
  '10261': {'GT': 2, 'Acc': '  3.33%', 'Pred': [19, 10, 1], 'edfname': '00729370_190214'},
  '00436': {'GT': 1, 'Acc': ' 46.67%', 'Pred': [16, 14, 0], 'edfname': '00127458_150618'},
  '13351': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00816768_260618'},
  '19625': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 2, 6], 'edfname': '01025734_280715'},
  '32424': {'GT': 0, 'Acc': ' 60.00%', 'Pred': [18, 7, 5], 'edfname': '01338710_261017'},
  '23708': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01149084_090414'},
  '22716': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [0, 2, 28], 'edfname': '01139924_300315'},
  '22850': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01139944_040214'},
  '08824': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00692685_110919'},
  '33942': {'GT': 0, 'Acc': ' 36.67%', 'Pred': [11, 4, 15], 'edfname': '01368637_230218'},
  '27901': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [4, 26, 0], 'edfname': '01251650_191219'},
  '20930': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01086373_290415'},
  '28760': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 0, 1], 'edfname': '01271175_270416'},
  '00514': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 4, 0], 'edfname': '00138259_030518'},
  '10165': {'GT': 1, 'Acc': ' 73.33%', 'Pred': [8, 22, 0], 'edfname': '00725683_091115'},
  '21679': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01128572_120314'},
  '29908': {'GT': 1, 'Acc': ' 73.33%', 'Pred': [8, 22, 0], 'edfname': '01293646_120719'},
  '13882': {'GT': 2, 'Acc': ' 63.33%', 'Pred': [11, 0, 19], 'edfname': '00840267_160317'},
  '31278': {'GT': 1, 'Acc': ' 26.67%', 'Pred': [22, 8, 0], 'edfname': '01311956_130117'},
  '04994': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00608836_270219'},
  '20036': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '01035162_180119'},
  '10945': {'GT': 2, 'Acc': ' 90.00%', 'Pred': [2, 1, 27], 'edfname': '00743464_220316'},
  '34612': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01392274_020320'},
  '15723': {'GT': 1, 'Acc': ' 23.33%', 'Pred': [4, 7, 19], 'edfname': '00899695_280918'},
  '14234': {'GT': 1, 'Acc': ' 46.67%', 'Pred': [16, 14, 0], 'edfname': '00851511_101018'},
  '06879': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00649687_010715'},
  '25021': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01182545_070316'},
  '06278': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [0, 28, 2], 'edfname': '00634366_241019'},
  '30252': {'GT': 1, 'Acc': ' 16.67%', 'Pred': [2, 5, 23], 'edfname': '01296557_160120'},
  '15581': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '00896386_240914'},
  '08686': {'GT': 1, 'Acc': ' 10.00%', 'Pred': [6, 3, 21], 'edfname': '00687089_011216'},
  '25030': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01182608_201014'},
  '34666': {'GT': 0, 'Acc': ' 63.33%', 'Pred': [19, 11, 0], 'edfname': '01392607_260718'},
  '09618': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00713432_210519'},
  '20418': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01063007_171115'},
  '02019': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00316358_050815'},
  '14142': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00848071_121018'},
  '16838': {'GT': 1, 'Acc': ' 73.33%', 'Pred': [0, 22, 8], 'edfname': '00941070_050617'},
  '25113': {'GT': 1, 'Acc': ' 76.67%', 'Pred': [7, 23, 0], 'edfname': '01186214'},
  '05423': {'GT': 1, 'Acc': ' 56.67%', 'Pred': [11, 17, 2], 'edfname': '00617283_230318'},
  '15420': {'GT': 1, 'Acc': ' 23.33%', 'Pred': [23, 7, 0], 'edfname': '00891889_060717'},
  '25301': {'GT': 0, 'Acc': ' 26.67%', 'Pred': [8, 22, 0], 'edfname': '01191562_091214'},
  '26001': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01207886_270515'},
  '30917': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [6, 24, 0], 'edfname': '01306496_130117'},
  '16383': {'GT': 1, 'Acc': ' 43.33%', 'Pred': [17, 13, 0], 'edfname': '00919596_200117'},
  '14816': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00875350_140514'},
  '05365': {'GT': 2, 'Acc': ' 70.00%', 'Pred': [0, 9, 21], 'edfname': '00616193_090218'},
  '10113': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 5, 0], 'edfname': '00723144_180118'},
  '11778': {'GT': 2, 'Acc': ' 70.00%', 'Pred': [1, 8, 21], 'edfname': '00763580_281118'},
  '04576': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00602435_270217'},
  '19620': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01025734_250815'},
  '32313': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01338557_190717'},
  '00677': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00164098_021019'},
  '27923': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01252335_211016'},
  '07994': {'GT': 1, 'Acc': ' 30.00%', 'Pred': [0, 9, 21], 'edfname': '00673505_020419'},
  '12420': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00783510_301115'},
  '05874': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00626957_040417'},
  '07153': {'GT': 0, 'Acc': ' 20.00%', 'Pred': [6, 18, 6], 'edfname': '00654671_151018'},
  '06138': {'GT': 2, 'Acc': ' 50.00%', 'Pred': [3, 12, 15], 'edfname': '00631526_070716'},
  '03528': {'GT': 1, 'Acc': ' 76.67%', 'Pred': [0, 23, 7], 'edfname': '00430974_050315'},
  '35936': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '01422773_010419'},
  '29825': {'GT': 0, 'Acc': ' 50.00%', 'Pred': [15, 4, 11], 'edfname': '01289759_110517'},
  '35116': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01403371_201218'},
  '03465': {'GT': 2, 'Acc': ' 56.67%', 'Pred': [13, 0, 17], 'edfname': '00430821_031116'},
  '25578': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01200248_290120'},
  '03422': {'GT': 0, 'Acc': ' 50.00%', 'Pred': [15, 15, 0], 'edfname': '00429185_130917'},
  '06523': {'GT': 1, 'Acc': ' 60.00%', 'Pred': [12, 18, 0], 'edfname': '00641692_170315'},
  '09978': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00721815_280519'},
  '00661': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '00162025_221215'},
  '15495': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00894185_250817'},
  '24372': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 0, 8], 'edfname': '01159816_310315'},
  '24503': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [0, 26, 4], 'edfname': '01164481_220615'},
  '30275': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01297715_101116'},
  '26132': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01209182_270415'},
  '30324': {'GT': 2, 'Acc': ' 83.33%', 'Pred': [3, 2, 25], 'edfname': '01297810_151116'},
  '13416': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '00817022_010415'},
  '01243': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00241549_060417'},
  '00189': {'GT': 2, 'Acc': ' 90.00%', 'Pred': [3, 0, 27], 'edfname': '00050941_130116'},
  '31647': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01321744_130417'},
  '24331': {'GT': 0, 'Acc': ' 53.33%', 'Pred': [16, 14, 0], 'edfname': '01159816_060515'},
  '31401': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01314894_190517'},
  '15503': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00894185_250817'},
  '12813': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00796775_281015'},
  '03074': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00406530_140819'},
  '07967': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00673491_141014'},
  '00311': {'GT': 0, 'Acc': ' 60.00%', 'Pred': [18, 1, 11], 'edfname': '00110793_210917'},
  '03230': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00414635_240414'},
  '00509': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00138259_030518'},
  '03734': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00472015_120314'},
  '29118': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '01277193_080616'},
  '01042': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00223407_161219'},
  '23354': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [6, 24, 0], 'edfname': '01146683_300419'},
  '07559': {'GT': 2, 'Acc': ' 36.67%', 'Pred': [8, 11, 11], 'edfname': '00665084_280219'},
  '23373': {'GT': 1, 'Acc': '  0.00%', 'Pred': [30, 0, 0], 'edfname': '01146683_300419'},
  '09099': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [0, 25, 5], 'edfname': '00698863_020419'},
  '14847': {'GT': 1, 'Acc': ' 50.00%', 'Pred': [8, 15, 7], 'edfname': '00876310_261217'},
  '19480': {'GT': 0, 'Acc': ' 50.00%', 'Pred': [15, 2, 13], 'edfname': '01018913_161018'},
  '25217': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01190283_261114'},
  '08171': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00675516_231019'},
  '15750': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '00899964_060115'},
  '22623': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01139924_060417'},
  '05125': {'GT': 0, 'Acc': ' 16.67%', 'Pred': [5, 24, 1], 'edfname': '00611999_041018'},
  '22075': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01134450_161019'},
  '13418': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00817022_010415'},
  '18422': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00980362_020616'},
  '09979': {'GT': 2, 'Acc': ' 60.00%', 'Pred': [0, 12, 18], 'edfname': '00721815_280519'},
  '17223': {'GT': 1, 'Acc': ' 66.67%', 'Pred': [1, 20, 9], 'edfname': '00951808_251116'},
  '31648': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 5, 0], 'edfname': '01321744_130417'},
  '09513': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '00709574_191217'},
  '07847': {'GT': 1, 'Acc': ' 76.67%', 'Pred': [0, 23, 7], 'edfname': '00671379_290617'},
  '12825': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '00796775_301115'},
  '11181': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00752045_260419'},
  '00929': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00209742_081217'},
  '36338': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01450766_200919'},
  '06268': {'GT': 1, 'Acc': ' 40.00%', 'Pred': [0, 12, 18], 'edfname': '00634366_241019'},
  '04307': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 0, 3], 'edfname': '00518633_031215'},
  '20355': {'GT': 0, 'Acc': ' 46.67%', 'Pred': [14, 16, 0], 'edfname': '01057134_240216'},
  '12887': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00799528_270916'},
  '19570': {'GT': 2, 'Acc': ' 63.33%', 'Pred': [0, 11, 19], 'edfname': '01020558_160316'},
  '02834': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00396894_030119'},
  '04574': {'GT': 0, 'Acc': ' 80.00%', 'Pred': [24, 6, 0], 'edfname': '00602435_270217'},
  '14926': {'GT': 2, 'Acc': ' 23.33%', 'Pred': [23, 0, 7], 'edfname': '00879020_080817'},
  '14197': {'GT': 1, 'Acc': ' 46.67%', 'Pred': [16, 14, 0], 'edfname': '00851302_261217'},
  '02999': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [0, 28, 2], 'edfname': '00401196_061218'},
  '23147': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01141791_270214'},
  '13061': {'GT': 1, 'Acc': ' 56.67%', 'Pred': [6, 17, 7], 'edfname': '00805584_060717'},
  '31869': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [0, 27, 3], 'edfname': '01329562_150518'},
  '23936': {'GT': 0, 'Acc': ' 20.00%', 'Pred': [6, 19, 5], 'edfname': '01154159_230414'},
  '07944': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00673491_141014'},
  '01827': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '00295596_101016'},
  '25823': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '01203444_090819'},
  '10075': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [0, 25, 5], 'edfname': '00723110_240419'},
  '18044': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00963680_280519'},
  '34631': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [0, 2, 28], 'edfname': '01392274_020320'},
  '22523': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01138302_160114'},
  '24507': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '01164481_220615'},
  '05715': {'GT': 1, 'Acc': ' 46.67%', 'Pred': [12, 14, 4], 'edfname': '00621729_020616'},
  '00011': {'GT': 1, 'Acc': ' 46.67%', 'Pred': [16, 14, 0], 'edfname': '00001809_261018'},
  '35078': {'GT': 1, 'Acc': ' 50.00%', 'Pred': [0, 15, 15], 'edfname': '01403151_080319'},
  '14684': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00871822_070918'},
  '26070': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01209181_250315'},
  '23569': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 0, 5], 'edfname': '01147101_190314'},
  '16968': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [0, 2, 28], 'edfname': '00948785_120116'},
  '03748': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [2, 27, 1], 'edfname': '00474121_161118'},
  '01665': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '00287432_091219'},
  '13680': {'GT': 0, 'Acc': ' 80.00%', 'Pred': [24, 6, 0], 'edfname': '00825385_130918'},
  '18958': {'GT': 1, 'Acc': ' 30.00%', 'Pred': [0, 9, 21], 'edfname': '00996941_180517'},
  '36312': {'GT': 2, 'Acc': ' 80.00%', 'Pred': [3, 3, 24], 'edfname': '01446092_160819'},
  '32700': {'GT': 2, 'Acc': ' 73.33%', 'Pred': [0, 8, 22], 'edfname': '01344212_240817'},
  '27351': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 4, 0], 'edfname': '01241945_231215'},
  '30750': {'GT': 1, 'Acc': ' 66.67%', 'Pred': [5, 20, 5], 'edfname': '01303479_290618'},
  '28846': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [0, 26, 4], 'edfname': '01272370_271217'},
  '24387': {'GT': 0, 'Acc': ' 63.33%', 'Pred': [19, 1, 10], 'edfname': '01159816_310315'},
  '08575': {'GT': 1, 'Acc': ' 26.67%', 'Pred': [22, 8, 0], 'edfname': '00685191_100419'},
  '22652': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01139924_140717'},
  '03762': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [6, 24, 0], 'edfname': '00474121_161118'},
  '12762': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00796686_090819'},
  '12336': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [0, 28, 2], 'edfname': '00779511_050319'},
  '02481': {'GT': 0, 'Acc': ' 33.33%', 'Pred': [10, 16, 4], 'edfname': '00354396_021116'},
  '19531': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01020014_151215'},
  '27536': {'GT': 0, 'Acc': ' 26.67%', 'Pred': [8, 22, 0], 'edfname': '01242981_161115'},
  '05156': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '00612060_221118'},
  '00021': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [3, 24, 3], 'edfname': '00001809_261018'},
  '32677': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01344212_220819'},
  '29259': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01279168_171219'},
  '04306': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00518633_031215'},
  '29277': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01279168_171219'},
  '28948': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01274934'},
  '15114': {'GT': 1, 'Acc': ' 66.67%', 'Pred': [1, 20, 9], 'edfname': '00885992_011216'},
  '31387': {'GT': 1, 'Acc': ' 10.00%', 'Pred': [27, 3, 0], 'edfname': '01314786_200317'},
  '36394': {'GT': 2, 'Acc': ' 26.67%', 'Pred': [0, 22, 8], 'edfname': '01457795_141019'},
  '18331': {'GT': 2, 'Acc': '  6.67%', 'Pred': [18, 10, 2], 'edfname': '00978276_281114'},
  '34809': {'GT': 0, 'Acc': ' 80.00%', 'Pred': [24, 6, 0], 'edfname': '01395459_210819'},
  '06191': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00633197_091215'},
  '04049': {'GT': 2, 'Acc': ' 70.00%', 'Pred': [1, 8, 21], 'edfname': '00489389_250319'},
  '05631': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [0, 26, 4], 'edfname': '00620551_141215'},
  '15083': {'GT': 1, 'Acc': ' 16.67%', 'Pred': [25, 5, 0], 'edfname': '00885959_160419'},
  '13970': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00840844_250119'},
  '16484': {'GT': 2, 'Acc': ' 60.00%', 'Pred': [0, 12, 18], 'edfname': '00926040_201119'},
  '18356': {'GT': 1, 'Acc': ' 76.67%', 'Pred': [6, 23, 1], 'edfname': '00978569_110119'},
  '11105': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00747390_130918'},
  '10822': {'GT': 1, 'Acc': ' 16.67%', 'Pred': [20, 5, 5], 'edfname': '00741780_281116'},
  '27610': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '01243157_141215'},
  '18437': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00980362_020616'},
  '20932': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01086373_290415'},
  '17806': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00959479_090217'},
  '27839': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01251384_041116'},
  '16782': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [0, 2, 28], 'edfname': '00939852_140214'},
  '06290': {'GT': 1, 'Acc': ' 60.00%', 'Pred': [0, 18, 12], 'edfname': '00634366_311019'},
  '30910': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [3, 27, 0], 'edfname': '01306496_130117'},
  '33933': {'GT': 0, 'Acc': ' 63.33%', 'Pred': [19, 5, 6], 'edfname': '01368637_230218'},
  '34925': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01398060_050918'},
  '16666': {'GT': 2, 'Acc': ' 86.67%', 'Pred': [0, 4, 26], 'edfname': '00933744_170519'},
  '32193': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01336270_040717'},
  '20429': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01063007_171115'},
  '08361': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '00678829_160217'},
  '11192': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00752045_260419'},
  '19271': {'GT': 0, 'Acc': ' 76.67%', 'Pred': [23, 7, 0], 'edfname': '01010278_281116'},
  '19048': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [0, 24, 6], 'edfname': '01006707_260319'},
  '25383': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '01195277_011119'},
  '25410': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '01195277_151119'},
  '06416': {'GT': 1, 'Acc': ' 70.00%', 'Pred': [6, 21, 3], 'edfname': '00635487_301019'},
  '15280': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [6, 24, 0], 'edfname': '00888613_080618'},
  '07110': {'GT': 1, 'Acc': ' 46.67%', 'Pred': [1, 14, 15], 'edfname': '00651389_281016'},
  '16737': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '00937775_270915'},
  '33444': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01357637_241117'},
  '13408': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '00817022_010415'},
  '19499': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01019016_241115'},
  '11031': {'GT': 2, 'Acc': ' 30.00%', 'Pred': [0, 21, 9], 'edfname': '00745209_220916'},
  '01729': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 1, 7], 'edfname': '00290547_051018'},
  '14304': {'GT': 1, 'Acc': ' 30.00%', 'Pred': [18, 9, 3], 'edfname': '00852650_170818'},
  '18754': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00987555_270717'},
  '27051': {'GT': 0, 'Acc': ' 76.67%', 'Pred': [23, 7, 0], 'edfname': '01235272_170915'},
  '02456': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00351872_250214'},
  '07771': {'GT': 0, 'Acc': ' 16.67%', 'Pred': [5, 25, 0], 'edfname': '00671212_020819'},
  '08968': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 4, 0], 'edfname': '00695147_250516'},
  '05134': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00611999_041018'},
  '12215': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00776486_121016'},
  '03666': {'GT': 0, 'Acc': ' 80.00%', 'Pred': [24, 6, 0], 'edfname': '00458897_141016'},
  '03549': {'GT': 1, 'Acc': ' 53.33%', 'Pred': [0, 16, 14], 'edfname': '00433859_230519'},
  '27593': {'GT': 1, 'Acc': ' 26.67%', 'Pred': [22, 8, 0], 'edfname': '01242984_211215'},
  '04165': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00502785_121219'},
  '22487': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01138298_220114'},
  '11135': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00747390_250718'},
  '16443': {'GT': 2, 'Acc': '  3.33%', 'Pred': [18, 11, 1], 'edfname': '00926040_061115'},
  '22103': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01134450_300919'},
  '33442': {'GT': 0, 'Acc': ' 63.33%', 'Pred': [19, 11, 0], 'edfname': '01357637_241117'},
  '01022': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '00220600_261114'},
  '02164': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00326875_050314'},
  '07652': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00666711_140218'},
  '06691': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00643806_130718'},
  '23895': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '01154075_230414'},
  '25101': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [4, 26, 0], 'edfname': '01186214'},
  '08449': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00682484_240718'},
  '26182': {'GT': 2, 'Acc': ' 76.67%', 'Pred': [7, 0, 23], 'edfname': '01210535_260515'},
  '13542': {'GT': 1, 'Acc': ' 13.33%', 'Pred': [0, 4, 26], 'edfname': '00817768_291118'},
  '34239': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01377415_200418'},
  '34236': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01376302_290318'},
  '28814': {'GT': 2, 'Acc': ' 86.67%', 'Pred': [0, 4, 26], 'edfname': '01271298_270319'},
  '30385': {'GT': 1, 'Acc': ' 36.67%', 'Pred': [0, 11, 19], 'edfname': '01298381_281016'},
  '10833': {'GT': 1, 'Acc': '  6.67%', 'Pred': [28, 2, 0], 'edfname': '00741780_281116'},
  '06517': {'GT': 1, 'Acc': ' 23.33%', 'Pred': [22, 7, 1], 'edfname': '00641692_170315'},
  '20462': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '01063145_220915'},
  '30594': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01301982_151116'},
  '22646': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01139924_060417'},
  '23087': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01141787_190515'},
  '12915': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00803113_260516'},
  '24383': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01159816_310315'},
  '11623': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00760780_141118'},
  '29390': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 2, 1], 'edfname': '01280762_171116'},
  '14452': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00861148_300119'},
  '15283': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00888613_080618'},
  '05833': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '00626173_030118'},
  '27683': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01246499_301115'},
  '20294': {'GT': 2, 'Acc': ' 60.00%', 'Pred': [0, 12, 18], 'edfname': '01054491_080116'},
  '02020': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '00316358_050815'},
  '16265': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00916613_240813'},
  '17901': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '00962086_240216'},
  '26957': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01235034_260220'},
  '16165': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [0, 24, 6], 'edfname': '00915077_310818'},
  '31028': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01306737_151216'},
  '27369': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01241947_261115'},
  '23951': {'GT': 0, 'Acc': '  0.00%', 'Pred': [0, 14, 16], 'edfname': '01154528_150514'},
  '07677': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00666711_150316'},
  '01180': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 4, 0], 'edfname': '00233423_111218'},
  '25840': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [1, 24, 5], 'edfname': '01203444_090819'},
  '20720': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01073369_130514'},
  '09228': {'GT': 1, 'Acc': ' 53.33%', 'Pred': [14, 16, 0], 'edfname': '00699668_310818'},
  '18961': {'GT': 1, 'Acc': ' 50.00%', 'Pred': [0, 15, 15], 'edfname': '00996941_180517'},
  '14758': {'GT': 0, 'Acc': ' 26.67%', 'Pred': [8, 22, 0], 'edfname': '00875320_070415'},
  '32944': {'GT': 2, 'Acc': ' 50.00%', 'Pred': [6, 9, 15], 'edfname': '01348263_210917'},
  '03069': {'GT': 2, 'Acc': ' 76.67%', 'Pred': [7, 0, 23], 'edfname': '00406530_140819'},
  '10772': {'GT': 1, 'Acc': ' 36.67%', 'Pred': [18, 11, 1], 'edfname': '00740694_110315'},
  '13899': {'GT': 2, 'Acc': ' 63.33%', 'Pred': [9, 2, 19], 'edfname': '00840267_160317'},
  '23639': {'GT': 0, 'Acc': ' 53.33%', 'Pred': [16, 14, 0], 'edfname': '01149073_170414'},
  '28671': {'GT': 1, 'Acc': ' 80.00%', 'Pred': [6, 24, 0], 'edfname': '01269786_071019'},
  '13845': {'GT': 1, 'Acc': ' 30.00%', 'Pred': [20, 9, 1], 'edfname': '00839295_150318'},
  '05040': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00609184_070618'},
  '16542': {'GT': 0, 'Acc': ' 16.67%', 'Pred': [5, 25, 0], 'edfname': '00927352_070316'},
  '20468': {'GT': 0, 'Acc': ' 80.00%', 'Pred': [24, 6, 0], 'edfname': '01063145_220915'},
  '35888': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01420059_050319'},
  '21024': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01090903_250917'},
  '08375': {'GT': 1, 'Acc': ' 30.00%', 'Pred': [3, 9, 18], 'edfname': '00678916_020718'},
  '06371': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00635370_170220'},
  '09613': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [0, 25, 5], 'edfname': '00713432_210519'},
  '14901': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [2, 27, 1], 'edfname': '00877170_301214'},
  '10041': {'GT': 0, 'Acc': ' 76.67%', 'Pred': [23, 7, 0], 'edfname': '00722511_031117'},
  '06079': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00628699_210519'},
  '21982': {'GT': 1, 'Acc': ' 56.67%', 'Pred': [13, 17, 0], 'edfname': '01133832_010716'},
  '30951': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 4, 0], 'edfname': '01306520_200117'},
  '29219': {'GT': 2, 'Acc': ' 86.67%', 'Pred': [1, 3, 26], 'edfname': '01277998_220217'},
  '22693': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01139924_291214'},
  '03281': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '00417769_100117'},
  '25208': {'GT': 0, 'Acc': ' 66.67%', 'Pred': [20, 7, 3], 'edfname': '01189271_031215'},
  '18704': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00986061_270116'},
  '34537': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01386864_220618'},
  '35086': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '01403151_080319'},
  '20277': {'GT': 2, 'Acc': ' 33.33%', 'Pred': [2, 18, 10], 'edfname': '01054491_080116'},
  '11691': {'GT': 2, 'Acc': ' 63.33%', 'Pred': [11, 0, 19], 'edfname': '00762439_081217'},
  '28159': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01256391_240216'},
  '25188': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01189271_031215'},
  '16084': {'GT': 1, 'Acc': ' 43.33%', 'Pred': [17, 13, 0], 'edfname': '00907971_030217'},
  '18809': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00988278_020819'},
  '08160': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00675516_190919'},
  '11444': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00756447_020817'},
  '06679': {'GT': 0, 'Acc': ' 80.00%', 'Pred': [24, 6, 0], 'edfname': '00643806_130718'},
  '11215': {'GT': 2, 'Acc': ' 76.67%', 'Pred': [0, 7, 23], 'edfname': '00752398_110214'},
  '30465': {'GT': 1, 'Acc': ' 53.33%', 'Pred': [5, 16, 9], 'edfname': '01299709_090419'},
  '29264': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01279168_171219'},
  '19712': {'GT': 2, 'Acc': ' 16.67%', 'Pred': [16, 9, 5], 'edfname': '01026990_241215'},
  '27249': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 5, 0], 'edfname': '01240162_121015'},
  '04817': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00606315_300715'},
  '30223': {'GT': 1, 'Acc': ' 56.67%', 'Pred': [9, 17, 4], 'edfname': '01296557_101116'},
  '31031': {'GT': 0, 'Acc': ' 63.33%', 'Pred': [19, 11, 0], 'edfname': '01306737_151216'},
  '13405': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 4, 1], 'edfname': '00817022_010415'},
  '15088': {'GT': 1, 'Acc': ' 16.67%', 'Pred': [23, 5, 2], 'edfname': '00885992_011216'},
  '15097': {'GT': 1, 'Acc': '  3.33%', 'Pred': [20, 1, 9], 'edfname': '00885992_011216'},
  '05355': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00616193_090218'},
  '22949': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01139951_060214'},
  '05499': {'GT': 1, 'Acc': ' 10.00%', 'Pred': [0, 3, 27], 'edfname': '00617893_231018'},
  '15921': {'GT': 2, 'Acc': ' 46.67%', 'Pred': [0, 16, 14], 'edfname': '00906868_071216'},
  '16925': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '00945348_170215'},
  '30652': {'GT': 0, 'Acc': '  0.00%', 'Pred': [0, 30, 0], 'edfname': '01302961_060117'},
  '07941': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [2, 25, 3], 'edfname': '00672867_031116'},
  '24073': {'GT': 0, 'Acc': ' 66.67%', 'Pred': [20, 10, 0], 'edfname': '01155730_070514'},
  '26888': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01235034_021117'},
  '30361': {'GT': 1, 'Acc': ' 30.00%', 'Pred': [0, 9, 21], 'edfname': '01298381_281016'},
  '18110': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [1, 25, 4], 'edfname': '00968332_310518'},
  '32504': {'GT': 1, 'Acc': ' 60.00%', 'Pred': [0, 18, 12], 'edfname': '01339759_211119'},
  '23974': {'GT': 0, 'Acc': ' 36.67%', 'Pred': [11, 19, 0], 'edfname': '01154834_011019'},
  '22873': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01139945_040314'},
  '24209': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01156406_120319'},
  '31738': {'GT': 0, 'Acc': ' 36.67%', 'Pred': [11, 7, 12], 'edfname': '01324136_201217'},
  '33934': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 4, 0], 'edfname': '01368637_230218'},
  '13875': {'GT': 2, 'Acc': ' 63.33%', 'Pred': [3, 8, 19], 'edfname': '00840267_160317'},
  '14067': {'GT': 0, 'Acc': ' 76.67%', 'Pred': [23, 5, 2], 'edfname': '00847087_220219'},
  '32438': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01338710_261017'},
  '22372': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01137650_290819'},
  '12209': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00776486_121016'},
  '25868': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '01203444_240719'},
  '15490': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [0, 2, 28], 'edfname': '00894185_250817'},
  '09107': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00698863_020419'},
  '27033': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 4, 0], 'edfname': '01235272_051115'},
  '11039': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [0, 2, 28], 'edfname': '00745209_220916'},
  '05939': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00626957_250216'},
  '07157': {'GT': 0, 'Acc': ' 60.00%', 'Pred': [18, 5, 7], 'edfname': '00654671_151018'},
  '03735': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00472015_120314'},
  '27224': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [0, 25, 5], 'edfname': '01238327_050819'},
  '10173': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00725683_091115'},
  '07538': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '00664961_261218'},
  '18158': {'GT': 1, 'Acc': ' 60.00%', 'Pred': [0, 18, 12], 'edfname': '00970997_301018'},
  '16072': {'GT': 1, 'Acc': ' 26.67%', 'Pred': [22, 8, 0], 'edfname': '00907971_030217'},
  '30114': {'GT': 1, 'Acc': ' 36.67%', 'Pred': [19, 11, 0], 'edfname': '01296342_141116'},
  '22673': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01139924_140717'},
  '22261': {'GT': 1, 'Acc': '  3.33%', 'Pred': [29, 1, 0], 'edfname': '01135545_230715'},
  '14330': {'GT': 0, 'Acc': '  0.00%', 'Pred': [0, 30, 0], 'edfname': '00853892_020519'},
  '27382': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '01241947_301215'},
  '26062': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01209181_250315'},
  '03248': {'GT': 0, 'Acc': ' 53.33%', 'Pred': [16, 14, 0], 'edfname': '00416942_190516'},
  '23908': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01154140_240414'},
  '02484': {'GT': 0, 'Acc': ' 40.00%', 'Pred': [12, 4, 14], 'edfname': '00354396_021116'},
  '07608': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00665821_260815'},
  '27937': {'GT': 2, 'Acc': ' 76.67%', 'Pred': [0, 7, 23], 'edfname': '01252335_211016'},
  '16859': {'GT': 1, 'Acc': ' 16.67%', 'Pred': [0, 5, 25], 'edfname': '00943284_210518'},
  '11448': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '00758349_270418'},
  '05583': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [0, 27, 3], 'edfname': '00619387_070317'},
  '28548': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 4, 0], 'edfname': '01266696_110516'},
  '23728': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01149087_170414'},
  '23182': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01141794_110214'},
  '34542': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01386864_220618'},
  '10256': {'GT': 2, 'Acc': ' 76.67%', 'Pred': [0, 7, 23], 'edfname': '00729370_190214'},
  '13858': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00840062_080519'},
  '07531': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '00664961_261218'},
  '00117': {'GT': 2, 'Acc': ' 46.67%', 'Pred': [0, 16, 14], 'edfname': '00048377_070916'},
  '26569': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '01220269_141218'},
  '31413': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [3, 27, 0], 'edfname': '01314894_190517'},
  '13133': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [0, 27, 3], 'edfname': '00809059_030715'},
  '13727': {'GT': 0, 'Acc': ' 63.33%', 'Pred': [19, 2, 9], 'edfname': '00834803_280819'},
  '34024': {'GT': 2, 'Acc': ' 63.33%', 'Pred': [4, 7, 19], 'edfname': '01370156_200218'},
  '17210': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00951808_251116'},
  '16419': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '00925720_290414'},
  '05073': {'GT': 2, 'Acc': ' 80.00%', 'Pred': [0, 6, 24], 'edfname': '00609255_140417'},
  '22229': {'GT': 1, 'Acc': ' 76.67%', 'Pred': [7, 23, 0], 'edfname': '01135545_160117'},
  '26444': {'GT': 1, 'Acc': ' 63.33%', 'Pred': [11, 19, 0], 'edfname': '01216443_240518'},
  '17810': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '00959479_090217'},
  '14511': {'GT': 2, 'Acc': ' 70.00%', 'Pred': [2, 7, 21], 'edfname': '00862802_260618'},
  '09630': {'GT': 1, 'Acc': ' 43.33%', 'Pred': [2, 13, 15], 'edfname': '00713699_290318'},
  '26519': {'GT': 0, 'Acc': ' 66.67%', 'Pred': [20, 10, 0], 'edfname': '01218042_121016'},
  '14371': {'GT': 2, 'Acc': ' 83.33%', 'Pred': [1, 4, 25], 'edfname': '00856756_281119'},
  '30065': {'GT': 0, 'Acc': '  0.00%', 'Pred': [0, 30, 0], 'edfname': '01296102_150217'},
  '00941': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00209742_081217'},
  '32608': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '01340109_030817'},
  '23031': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '01141786_190214'},
  '21496': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01124522_170818'},
  '28150': {'GT': 0, 'Acc': ' 40.00%', 'Pred': [12, 18, 0], 'edfname': '01254424_120118'},
  '29895': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01291791_100217'},
  '21556': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 0, 4], 'edfname': '01126091_200515'},
  '15018': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00885321_170314'},
  '12585': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00788666_011118'},
  '17822': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00959479_090217'},
  '16396': {'GT': 1, 'Acc': ' 30.00%', 'Pred': [21, 9, 0], 'edfname': '00919596_200117'},
  '20230': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01043928_210415'},
  '06497': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00640147_140519'},
  '04311': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00518633_311215'},
  '31093': {'GT': 1, 'Acc': ' 66.67%', 'Pred': [3, 20, 7], 'edfname': '01307883_100217'},
  '17920': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00962086_270116'},
  '13483': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '00817092_020118'},
  '19713': {'GT': 2, 'Acc': '  0.00%', 'Pred': [6, 24, 0], 'edfname': '01026990_241215'},
  '24748': {'GT': 1, 'Acc': ' 46.67%', 'Pred': [14, 14, 2], 'edfname': '01174216_090516'},
  '27117': {'GT': 0, 'Acc': ' 56.67%', 'Pred': [17, 13, 0], 'edfname': '01235281_210915'},
  '34465': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01382697_261218'},
  '28391': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01264838_110416'},
  '17663': {'GT': 2, 'Acc': ' 86.67%', 'Pred': [4, 0, 26], 'edfname': '00956561_241116'},
  '20524': {'GT': 2, 'Acc': ' 76.67%', 'Pred': [7, 0, 23], 'edfname': '01064740_231214'},
  '15333': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00889595_240419'},
  '22255': {'GT': 1, 'Acc': ' 53.33%', 'Pred': [14, 16, 0], 'edfname': '01135545_230715'},
  '30251': {'GT': 1, 'Acc': ' 10.00%', 'Pred': [0, 3, 27], 'edfname': '01296557_160120'},
  '08835': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00692685_110919'},
  '08729': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00687918_020318'},
  '20945': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '01090425_290419'},
  '30129': {'GT': 1, 'Acc': ' 10.00%', 'Pred': [27, 3, 0], 'edfname': '01296342_141116'},
  '34095': {'GT': 1, 'Acc': ' 13.33%', 'Pred': [0, 4, 26], 'edfname': '01372947_240518'},
  '28477': {'GT': 2, 'Acc': ' 83.33%', 'Pred': [3, 2, 25], 'edfname': '01264980_200516'},
  '20492': {'GT': 2, 'Acc': ' 70.00%', 'Pred': [0, 9, 21], 'edfname': '01064650_161117'},
  '18695': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00986061_270116'},
  '18532': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [4, 26, 0], 'edfname': '00984999_070617'},
  '16966': {'GT': 2, 'Acc': ' 70.00%', 'Pred': [4, 5, 21], 'edfname': '00948785_120116'},
  '19729': {'GT': 2, 'Acc': ' 80.00%', 'Pred': [0, 6, 24], 'edfname': '01026990_241215'},
  '12469': {'GT': 1, 'Acc': ' 53.33%', 'Pred': [14, 16, 0], 'edfname': '00784417_100315'},
  '04286': {'GT': 0, 'Acc': ' 80.00%', 'Pred': [24, 6, 0], 'edfname': '00507286_270715'},
  '08571': {'GT': 1, 'Acc': ' 46.67%', 'Pred': [16, 14, 0], 'edfname': '00685191_100419'},
  '32107': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [0, 27, 3], 'edfname': '01334787_211117'},
  '29409': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '01280762_171116'},
  '11460': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00758349_270418'},
  '09992': {'GT': 1, 'Acc': ' 23.33%', 'Pred': [23, 7, 0], 'edfname': '00722143_261118'},
  '28686': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [5, 25, 0], 'edfname': '01269786_071019'},
  '00420': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 0, 1], 'edfname': '00126875_260515'},
  '31013': {'GT': 0, 'Acc': ' 40.00%', 'Pred': [12, 18, 0], 'edfname': '01306737_151216'},
  '01003': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00213418_210115'},
  '07285': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00657399_070515'},
  '21102': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 4, 0], 'edfname': '01096498_270718'},
  '32014': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 5, 0], 'edfname': '01333917_210918'},
  '08955': {'GT': 2, 'Acc': ' 76.67%', 'Pred': [0, 7, 23], 'edfname': '00695058_191017'},
  '01995': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00315506_250715'},
  '16694': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '00935598_301018'},
  '04320': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00518633_311215'},
  '11168': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00752045_260419'},
  '31307': {'GT': 2, 'Acc': ' 30.00%', 'Pred': [0, 21, 9], 'edfname': '01312293_120417'},
  '21114': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 5, 0], 'edfname': '01096498_270718'},
  '30581': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01301982_151116'}}

1D CNN: M5-like model

class M5(nn.Module):
    def __init__(self, n_input=20, n_output=3, stride=4, n_channel=256, 
                 use_age=True, final_pool='average'):
        super().__init__()
        
        if final_pool not in {'average', 'max'}:
            raise ValueError("final_pool must be set to one of ['average', 'max']")
        
        self.use_age = use_age
        
        self.conv1 = nn.Conv1d(n_input, n_channel, kernel_size=41, stride=3)
        self.bn1 = nn.BatchNorm1d(n_channel)
        self.pool1 = nn.MaxPool1d(2)
        
        self.conv2 = nn.Conv1d(n_channel, n_channel, kernel_size=11)
        self.bn2 = nn.BatchNorm1d(n_channel)
        self.pool2 = nn.MaxPool1d(2)
        
        self.conv3 = nn.Conv1d(n_channel, 2 * n_channel, kernel_size=11)
        self.bn3 = nn.BatchNorm1d(2 * n_channel)
        self.pool3 = nn.MaxPool1d(2)
        
        self.conv4 = nn.Conv1d(2 * n_channel, 2 * n_channel, kernel_size=11)
        self.bn4 = nn.BatchNorm1d(2 * n_channel)
        self.pool4 = nn.MaxPool1d(2)
        
        self.conv5 = nn.Conv1d(2 * n_channel, 2 * n_channel, kernel_size=11)
        self.bn5 = nn.BatchNorm1d(2 * n_channel)
        self.pool5 = nn.MaxPool1d(2)
        
        if final_pool == 'average':
            self.final_pool = nn.AdaptiveAvgPool1d(1)
        elif final_pool == 'max':
            self.final_pool = nn.AdaptiveMaxPool1d(1)
    
        if self.use_age:        
            self.fc1 = nn.Linear(2 * n_channel + 1, 2 * n_channel)
        else:
            self.fc1 = nn.Linear(2 * n_channel, 2 * n_channel)
            
        self.dropout = nn.Dropout(p=0.3)
        self.bnfc1 = nn.BatchNorm1d(2 * n_channel)
        self.fc2 = nn.Linear(2 * n_channel, n_output)
        
    def reset_weights(self):
        for m in self.modules():
            if hasattr(m, 'reset_parameters'):
                m.reset_parameters()

    def forward(self, x, age):
        # conv-bn-relu-pool 
        x = self.conv1(x)
        x = F.relu(self.bn1(x))
        x = self.pool1(x)

        x = self.conv2(x)
        x = F.relu(self.bn2(x))
        x = self.pool2(x)

        x = self.conv3(x)
        x = F.relu(self.bn3(x))
        x = self.pool3(x)

        x = self.conv4(x)
        x = F.relu(self.bn4(x))
        x = self.pool4(x)

        x = self.conv5(x)
        x = F.relu(self.bn5(x))
        x = self.pool5(x)
    
        x = self.final_pool(x).squeeze()
        if self.use_age:
            x = torch.cat((x, age.reshape(-1, 1)), dim=1)

        # fc-bn-dropout-relu-fc
        x = self.fc1(x)
        x = self.bnfc1(x)
        x = self.dropout(x)
        x = F.relu(x)
        x = self.fc2(x)
        
        return x
        # return F.log_softmax(x, dim=1)

model = M5(n_input=train_dataset[0]['signal'].shape[0], 
           n_output=3, 
           use_age=True, 
           final_pool='max')

model = model.to(device, dtype=torch.float32)
print(model)
print()

# tensorboard visualization
visualize_network_tensorboard(model, 'M5-like')

# number of parameters
n = count_parameters(model)
print(f'The Number of parameters of the model: {n:,}')

M5(
  (conv1): Conv1d(20, 256, kernel_size=(41,), stride=(3,))
  (bn1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (pool1): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (conv2): Conv1d(256, 256, kernel_size=(11,), stride=(1,))
  (bn2): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (pool2): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (conv3): Conv1d(256, 512, kernel_size=(11,), stride=(1,))
  (bn3): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (pool3): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (conv4): Conv1d(512, 512, kernel_size=(11,), stride=(1,))
  (bn4): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (pool4): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (conv5): Conv1d(512, 512, kernel_size=(11,), stride=(1,))
  (bn5): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (pool5): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (final_pool): AdaptiveMaxPool1d(output_size=1)
  (fc1): Linear(in_features=513, out_features=512, bias=True)
  (dropout): Dropout(p=0.3, inplace=False)
  (bnfc1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (fc2): Linear(in_features=512, out_features=3, bias=True)
)

The Number of parameters of the model: 8,411,651

# record = learning_rate_search(model, 
#                               min_log_lr=-4.5, 
#                               max_log_lr=-1.0, 
#                               trials=500, 
#                               epochs=1)
# draw_learning_rate_record(record)
# best_log_lr = record[np.argmax(np.array([v for lr, v in record]))][0]

best_log_lr = -3.1867127146006564
print('best_log_lr:', best_log_lr)

best_log_lr: -3.1867127146006564

# reduce the learning after [lr_schedule_step] epochs by a factor of 10
n_epoch = 200
lr_schedule_step = 80
log_interval = len(train_loader) // 5

loss_history = []
train_acc_history = []
val_acc_history = []

model.reset_weights()
optimizer = optim.AdamW(model.parameters(), lr=10 ** best_log_lr, weight_decay=0.0001)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=lr_schedule_step, gamma=0.1)

print(f'{"*"*40} Training Starts {"*"*40}')

for epoch in tqdm(range(1, n_epoch + 1)):
    print(f'{"-"*30} Epoch {epoch:03d} / {n_epoch:03d}, Learning rate: {optimizer.param_groups[-1]["lr"]:.2e} {"-"*30}')
    
    # train
    loss, train_accuracy, train_confusion = train_one_epoch(model, optimizer, log_interval)
    loss_history.extend(loss)
    train_acc_history.append(train_accuracy)
    
    # validation
    val_accuracy, val_confusion = check_val_accuracy(model)
    val_acc_history.append(val_accuracy)
    
    # learning rate schedule
    scheduler.step()
    
    print()
    print(f'* Train accuracy / confusion: {train_accuracy:.2f}% / {train_confusion.tolist()}, ')
    print(f'* Val   accuracy / confusion: {val_accuracy:.2f}% / {val_confusion.tolist()}')
    print()
    
# test
test_accuracy, test_confusion, test_debug = check_test_accuracy(model, repeat=30)

print(f'{"*"*40} Training Ends {"*"*40}')
print(f'- Test accuracy: {test_accuracy:.2f}%')
print()
print('- Confusion matrix:\n', test_confusion)
print()

# draw the training loss plot
draw_loss_plot(loss_history)
draw_accuracy_history(train_acc_history, val_acc_history)

# draw the confusion matrix
draw_confusion(test_confusion)

**************************************** Training Starts ****************************************

------------------------------ Epoch 001 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 1.037536
- Iter 262 / 659, Loss: 0.919100
- Iter 393 / 659, Loss: 0.974410
- Iter 524 / 659, Loss: 0.878469
- Iter 655 / 659, Loss: 0.919213

* Train accuracy / confusion: 47.41% / [[5725, 2101, 720], [3132, 2978, 1379], [1572, 2186, 1295]], 
* Val   accuracy / confusion: 51.40% / [[665, 376, 28], [268, 597, 73], [105, 433, 95]]

------------------------------ Epoch 002 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.960407
- Iter 262 / 659, Loss: 0.955758
- Iter 393 / 659, Loss: 0.887325
- Iter 524 / 659, Loss: 1.173430
- Iter 655 / 659, Loss: 0.864117

* Train accuracy / confusion: 51.60% / [[5948, 2021, 574], [2903, 3078, 1506], [1112, 2091, 1855]], 
* Val   accuracy / confusion: 50.49% / [[636, 164, 269], [191, 191, 556], [47, 80, 506]]

------------------------------ Epoch 003 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.938929
- Iter 262 / 659, Loss: 0.905685
- Iter 393 / 659, Loss: 0.787679
- Iter 524 / 659, Loss: 0.803980
- Iter 655 / 659, Loss: 1.167931

* Train accuracy / confusion: 54.13% / [[6086, 1965, 493], [2651, 3301, 1537], [940, 2088, 2027]], 
* Val   accuracy / confusion: 54.77% / [[784, 243, 42], [310, 497, 131], [97, 371, 165]]

------------------------------ Epoch 004 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 1.113065
- Iter 262 / 659, Loss: 0.816073
- Iter 393 / 659, Loss: 0.912006
- Iter 524 / 659, Loss: 0.873578
- Iter 655 / 659, Loss: 0.869122

* Train accuracy / confusion: 56.42% / [[6178, 1863, 503], [2561, 3424, 1501], [826, 1937, 2295]], 
* Val   accuracy / confusion: 56.89% / [[886, 151, 32], [421, 395, 122], [170, 242, 221]]

------------------------------ Epoch 005 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.777914
- Iter 262 / 659, Loss: 1.016317
- Iter 393 / 659, Loss: 0.736934
- Iter 524 / 659, Loss: 0.820300
- Iter 655 / 659, Loss: 0.913428

* Train accuracy / confusion: 57.81% / [[6151, 1875, 515], [2447, 3587, 1457], [772, 1830, 2454]], 
* Val   accuracy / confusion: 58.90% / [[726, 292, 51], [224, 536, 178], [83, 257, 293]]

------------------------------ Epoch 006 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.899854
- Iter 262 / 659, Loss: 0.750488
- Iter 393 / 659, Loss: 0.705169
- Iter 524 / 659, Loss: 0.733839
- Iter 655 / 659, Loss: 0.921000

* Train accuracy / confusion: 59.33% / [[6192, 1868, 486], [2397, 3696, 1396], [730, 1700, 2623]], 
* Val   accuracy / confusion: 58.75% / [[936, 93, 40], [460, 328, 150], [195, 151, 287]]

------------------------------ Epoch 007 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.795140
- Iter 262 / 659, Loss: 0.722068
- Iter 393 / 659, Loss: 0.783028
- Iter 524 / 659, Loss: 0.676564
- Iter 655 / 659, Loss: 0.793411

* Train accuracy / confusion: 60.39% / [[6174, 1871, 498], [2305, 3796, 1385], [675, 1618, 2766]], 
* Val   accuracy / confusion: 61.44% / [[798, 178, 93], [275, 401, 262], [84, 126, 423]]

------------------------------ Epoch 008 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.936959
- Iter 262 / 659, Loss: 0.854431
- Iter 393 / 659, Loss: 0.702073
- Iter 524 / 659, Loss: 0.825950
- Iter 655 / 659, Loss: 0.851948

* Train accuracy / confusion: 61.41% / [[6180, 1919, 446], [2238, 3949, 1303], [620, 1612, 2821]], 
* Val   accuracy / confusion: 61.97% / [[849, 154, 66], [320, 446, 172], [108, 184, 341]]

------------------------------ Epoch 009 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 1.073724
- Iter 262 / 659, Loss: 0.894368
- Iter 393 / 659, Loss: 0.917166
- Iter 524 / 659, Loss: 0.934749
- Iter 655 / 659, Loss: 0.767456

* Train accuracy / confusion: 62.37% / [[6229, 1871, 444], [2223, 3977, 1288], [573, 1537, 2946]], 
* Val   accuracy / confusion: 63.86% / [[848, 193, 28], [304, 542, 92], [98, 239, 296]]

------------------------------ Epoch 010 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.947439
- Iter 262 / 659, Loss: 1.040163
- Iter 393 / 659, Loss: 0.790218
- Iter 524 / 659, Loss: 0.989247
- Iter 655 / 659, Loss: 0.835895

* Train accuracy / confusion: 64.18% / [[6270, 1830, 446], [2013, 4244, 1230], [555, 1479, 3021]], 
* Val   accuracy / confusion: 63.11% / [[781, 266, 22], [245, 627, 66], [97, 278, 258]]

------------------------------ Epoch 011 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 1.007012
- Iter 262 / 659, Loss: 0.964809
- Iter 393 / 659, Loss: 0.772453
- Iter 524 / 659, Loss: 0.761741
- Iter 655 / 659, Loss: 0.771415

* Train accuracy / confusion: 64.93% / [[6361, 1734, 451], [2067, 4195, 1226], [533, 1384, 3137]], 
* Val   accuracy / confusion: 56.74% / [[561, 224, 284], [126, 383, 429], [20, 59, 554]]

------------------------------ Epoch 012 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.821765
- Iter 262 / 659, Loss: 0.663269
- Iter 393 / 659, Loss: 1.018666
- Iter 524 / 659, Loss: 0.722849
- Iter 655 / 659, Loss: 0.718068

* Train accuracy / confusion: 66.61% / [[6485, 1602, 459], [1957, 4325, 1209], [546, 1268, 3237]], 
* Val   accuracy / confusion: 66.89% / [[656, 349, 64], [115, 684, 139], [35, 172, 426]]

------------------------------ Epoch 013 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.627722
- Iter 262 / 659, Loss: 0.584293
- Iter 393 / 659, Loss: 0.851160
- Iter 524 / 659, Loss: 0.733621
- Iter 655 / 659, Loss: 0.513802

* Train accuracy / confusion: 67.99% / [[6514, 1570, 459], [1883, 4462, 1146], [486, 1206, 3362]], 
* Val   accuracy / confusion: 65.38% / [[832, 165, 72], [277, 450, 211], [96, 93, 444]]

------------------------------ Epoch 014 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.666971
- Iter 262 / 659, Loss: 0.591790
- Iter 393 / 659, Loss: 0.719110
- Iter 524 / 659, Loss: 1.012515
- Iter 655 / 659, Loss: 0.739013

* Train accuracy / confusion: 69.93% / [[6663, 1463, 419], [1835, 4608, 1045], [459, 1120, 3476]], 
* Val   accuracy / confusion: 63.48% / [[748, 303, 18], [158, 762, 18], [59, 408, 166]]

------------------------------ Epoch 015 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.540431
- Iter 262 / 659, Loss: 0.604938
- Iter 393 / 659, Loss: 0.380226
- Iter 524 / 659, Loss: 0.572181
- Iter 655 / 659, Loss: 0.858820

* Train accuracy / confusion: 71.95% / [[6745, 1367, 429], [1674, 4834, 981], [448, 1016, 3594]], 
* Val   accuracy / confusion: 62.77% / [[762, 98, 209], [168, 300, 470], [22, 16, 595]]

------------------------------ Epoch 016 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.648151
- Iter 262 / 659, Loss: 0.656482
- Iter 393 / 659, Loss: 0.475896
- Iter 524 / 659, Loss: 0.638007
- Iter 655 / 659, Loss: 0.655234

* Train accuracy / confusion: 73.83% / [[6860, 1322, 360], [1602, 4986, 904], [408, 923, 3723]], 
* Val   accuracy / confusion: 70.11% / [[683, 288, 98], [98, 670, 170], [27, 108, 498]]

------------------------------ Epoch 017 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.663096
- Iter 262 / 659, Loss: 0.398195
- Iter 393 / 659, Loss: 0.515251
- Iter 524 / 659, Loss: 0.488443
- Iter 655 / 659, Loss: 0.462535

* Train accuracy / confusion: 75.79% / [[6960, 1227, 353], [1485, 5179, 830], [387, 824, 3843]], 
* Val   accuracy / confusion: 74.36% / [[874, 172, 23], [181, 715, 42], [75, 184, 374]]

------------------------------ Epoch 018 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.633729
- Iter 262 / 659, Loss: 0.731660
- Iter 393 / 659, Loss: 0.589060
- Iter 524 / 659, Loss: 0.791831
- Iter 655 / 659, Loss: 0.758658

* Train accuracy / confusion: 78.09% / [[7146, 1067, 331], [1431, 5390, 672], [341, 778, 3932]], 
* Val   accuracy / confusion: 77.99% / [[886, 158, 25], [138, 724, 76], [75, 109, 449]]

------------------------------ Epoch 019 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.598252
- Iter 262 / 659, Loss: 0.325215
- Iter 393 / 659, Loss: 0.374854
- Iter 524 / 659, Loss: 0.488468
- Iter 655 / 659, Loss: 0.754157

* Train accuracy / confusion: 80.19% / [[7236, 1007, 297], [1257, 5583, 650], [332, 634, 4092]], 
* Val   accuracy / confusion: 74.20% / [[989, 78, 2], [237, 694, 7], [137, 220, 276]]

------------------------------ Epoch 020 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.398277
- Iter 262 / 659, Loss: 0.523137
- Iter 393 / 659, Loss: 0.363212
- Iter 524 / 659, Loss: 0.544010
- Iter 655 / 659, Loss: 0.444187

* Train accuracy / confusion: 82.57% / [[7334, 936, 274], [1119, 5824, 546], [253, 548, 4254]], 
* Val   accuracy / confusion: 80.68% / [[840, 200, 29], [86, 784, 68], [26, 101, 506]]

------------------------------ Epoch 021 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.506603
- Iter 262 / 659, Loss: 0.601730
- Iter 393 / 659, Loss: 0.303905
- Iter 524 / 659, Loss: 0.313538
- Iter 655 / 659, Loss: 0.181769

* Train accuracy / confusion: 84.45% / [[7464, 832, 249], [994, 5992, 503], [236, 466, 4352]], 
* Val   accuracy / confusion: 82.08% / [[826, 172, 71], [75, 781, 82], [17, 56, 560]]

------------------------------ Epoch 022 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.259566
- Iter 262 / 659, Loss: 0.354789
- Iter 393 / 659, Loss: 0.327907
- Iter 524 / 659, Loss: 0.515642
- Iter 655 / 659, Loss: 0.496022

* Train accuracy / confusion: 86.31% / [[7560, 752, 228], [864, 6198, 429], [201, 413, 4443]], 
* Val   accuracy / confusion: 82.73% / [[1017, 43, 9], [169, 749, 20], [105, 110, 418]]

------------------------------ Epoch 023 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.309334
- Iter 262 / 659, Loss: 0.224712
- Iter 393 / 659, Loss: 0.334945
- Iter 524 / 659, Loss: 0.239537
- Iter 655 / 659, Loss: 0.330483

* Train accuracy / confusion: 87.69% / [[7669, 658, 208], [784, 6335, 376], [207, 363, 4488]], 
* Val   accuracy / confusion: 84.02% / [[870, 175, 24], [56, 859, 23], [22, 122, 489]]

------------------------------ Epoch 024 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.143061
- Iter 262 / 659, Loss: 0.295030
- Iter 393 / 659, Loss: 0.198586
- Iter 524 / 659, Loss: 0.486631
- Iter 655 / 659, Loss: 0.164156

* Train accuracy / confusion: 89.28% / [[7756, 596, 191], [694, 6466, 329], [150, 300, 4606]], 
* Val   accuracy / confusion: 87.99% / [[982, 70, 17], [98, 787, 53], [39, 40, 554]]

------------------------------ Epoch 025 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.324768
- Iter 262 / 659, Loss: 0.082862
- Iter 393 / 659, Loss: 0.326576
- Iter 524 / 659, Loss: 0.172530
- Iter 655 / 659, Loss: 0.147797

* Train accuracy / confusion: 90.54% / [[7852, 520, 170], [634, 6585, 271], [142, 257, 4657]], 
* Val   accuracy / confusion: 88.71% / [[943, 93, 33], [54, 807, 77], [15, 26, 592]]

------------------------------ Epoch 026 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.134867
- Iter 262 / 659, Loss: 0.065931
- Iter 393 / 659, Loss: 0.331405
- Iter 524 / 659, Loss: 0.177804
- Iter 655 / 659, Loss: 0.414276

* Train accuracy / confusion: 91.28% / [[7912, 479, 150], [580, 6655, 256], [129, 244, 4683]], 
* Val   accuracy / confusion: 85.15% / [[974, 92, 3], [70, 860, 8], [36, 183, 414]]

------------------------------ Epoch 027 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.105949
- Iter 262 / 659, Loss: 0.107801
- Iter 393 / 659, Loss: 0.388456
- Iter 524 / 659, Loss: 0.130386
- Iter 655 / 659, Loss: 0.071423

* Train accuracy / confusion: 92.07% / [[7951, 443, 147], [513, 6750, 229], [112, 229, 4714]], 
* Val   accuracy / confusion: 88.94% / [[959, 97, 13], [56, 834, 48], [23, 55, 555]]

------------------------------ Epoch 028 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.130950
- Iter 262 / 659, Loss: 0.097148
- Iter 393 / 659, Loss: 0.169707
- Iter 524 / 659, Loss: 0.104609
- Iter 655 / 659, Loss: 0.291529

* Train accuracy / confusion: 93.02% / [[8051, 379, 116], [438, 6831, 217], [128, 194, 4734]], 
* Val   accuracy / confusion: 90.87% / [[958, 92, 19], [45, 872, 21], [12, 52, 569]]

------------------------------ Epoch 029 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.085701
- Iter 262 / 659, Loss: 0.140414
- Iter 393 / 659, Loss: 0.292513
- Iter 524 / 659, Loss: 0.098748
- Iter 655 / 659, Loss: 0.146823

* Train accuracy / confusion: 93.84% / [[8088, 335, 125], [382, 6925, 184], [106, 168, 4775]], 
* Val   accuracy / confusion: 91.52% / [[1005, 49, 15], [58, 837, 43], [21, 38, 574]]

------------------------------ Epoch 030 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.151322
- Iter 262 / 659, Loss: 0.280409
- Iter 393 / 659, Loss: 0.044064
- Iter 524 / 659, Loss: 0.099679
- Iter 655 / 659, Loss: 0.166877

* Train accuracy / confusion: 94.24% / [[8124, 326, 94], [371, 6951, 167], [93, 163, 4799]], 
* Val   accuracy / confusion: 91.89% / [[971, 85, 13], [26, 900, 12], [13, 65, 555]]

------------------------------ Epoch 031 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.199179
- Iter 262 / 659, Loss: 0.094733
- Iter 393 / 659, Loss: 0.290515
- Iter 524 / 659, Loss: 0.294297
- Iter 655 / 659, Loss: 0.215616

* Train accuracy / confusion: 94.79% / [[8156, 293, 94], [323, 7026, 145], [89, 155, 4807]], 
* Val   accuracy / confusion: 91.10% / [[1051, 14, 4], [121, 784, 33], [35, 28, 570]]

------------------------------ Epoch 032 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.275452
- Iter 262 / 659, Loss: 0.054815
- Iter 393 / 659, Loss: 0.056502
- Iter 524 / 659, Loss: 0.224140
- Iter 655 / 659, Loss: 0.136634

* Train accuracy / confusion: 95.22% / [[8213, 255, 72], [329, 7018, 145], [85, 122, 4849]], 
* Val   accuracy / confusion: 93.07% / [[1009, 56, 4], [35, 882, 21], [31, 36, 566]]

------------------------------ Epoch 033 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.161949
- Iter 262 / 659, Loss: 0.222552
- Iter 393 / 659, Loss: 0.178279
- Iter 524 / 659, Loss: 0.111414
- Iter 655 / 659, Loss: 0.099758

* Train accuracy / confusion: 95.59% / [[8209, 239, 93], [271, 7097, 125], [83, 118, 4853]], 
* Val   accuracy / confusion: 93.18% / [[1013, 53, 3], [47, 886, 5], [21, 51, 561]]

------------------------------ Epoch 034 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.124480
- Iter 262 / 659, Loss: 0.096961
- Iter 393 / 659, Loss: 0.068288
- Iter 524 / 659, Loss: 0.032979
- Iter 655 / 659, Loss: 0.195784

* Train accuracy / confusion: 95.92% / [[8248, 218, 79], [250, 7126, 116], [82, 115, 4854]], 
* Val   accuracy / confusion: 90.19% / [[1052, 16, 1], [85, 846, 7], [67, 83, 483]]

------------------------------ Epoch 035 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.067064
- Iter 262 / 659, Loss: 0.013917
- Iter 393 / 659, Loss: 0.086497
- Iter 524 / 659, Loss: 0.027575
- Iter 655 / 659, Loss: 0.173128

* Train accuracy / confusion: 95.84% / [[8241, 223, 81], [248, 7109, 128], [75, 123, 4860]], 
* Val   accuracy / confusion: 93.11% / [[1046, 18, 5], [80, 816, 42], [24, 13, 596]]

------------------------------ Epoch 036 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.050159
- Iter 262 / 659, Loss: 0.025515
- Iter 393 / 659, Loss: 0.048347
- Iter 524 / 659, Loss: 0.129003
- Iter 655 / 659, Loss: 0.135208

* Train accuracy / confusion: 96.31% / [[8267, 204, 78], [217, 7164, 107], [67, 105, 4879]], 
* Val   accuracy / confusion: 95.34% / [[1033, 25, 11], [37, 880, 21], [12, 17, 604]]

------------------------------ Epoch 037 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.076145
- Iter 262 / 659, Loss: 0.026184
- Iter 393 / 659, Loss: 0.066645
- Iter 524 / 659, Loss: 0.080349
- Iter 655 / 659, Loss: 0.131632

* Train accuracy / confusion: 96.66% / [[8295, 186, 59], [230, 7175, 90], [56, 83, 4914]], 
* Val   accuracy / confusion: 91.33% / [[1061, 4, 4], [137, 779, 22], [42, 20, 571]]

------------------------------ Epoch 038 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.049313
- Iter 262 / 659, Loss: 0.119927
- Iter 393 / 659, Loss: 0.372548
- Iter 524 / 659, Loss: 0.006689
- Iter 655 / 659, Loss: 0.086273

* Train accuracy / confusion: 96.52% / [[8280, 195, 68], [215, 7186, 88], [68, 100, 4888]], 
* Val   accuracy / confusion: 95.30% / [[1036, 24, 9], [31, 882, 25], [18, 17, 598]]

------------------------------ Epoch 039 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.024044
- Iter 262 / 659, Loss: 0.070300
- Iter 393 / 659, Loss: 0.062855
- Iter 524 / 659, Loss: 0.130412
- Iter 655 / 659, Loss: 0.046808

* Train accuracy / confusion: 96.84% / [[8323, 151, 69], [180, 7205, 105], [78, 84, 4893]], 
* Val   accuracy / confusion: 93.07% / [[942, 119, 8], [15, 918, 5], [3, 33, 597]]

------------------------------ Epoch 040 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.027820
- Iter 262 / 659, Loss: 0.076101
- Iter 393 / 659, Loss: 0.017199
- Iter 524 / 659, Loss: 0.013969
- Iter 655 / 659, Loss: 0.246766

* Train accuracy / confusion: 97.21% / [[8349, 143, 52], [184, 7216, 86], [49, 74, 4935]], 
* Val   accuracy / confusion: 95.11% / [[1021, 37, 11], [27, 900, 11], [18, 25, 590]]

------------------------------ Epoch 041 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.047774
- Iter 262 / 659, Loss: 0.124159
- Iter 393 / 659, Loss: 0.027518
- Iter 524 / 659, Loss: 0.157489
- Iter 655 / 659, Loss: 0.071541

* Train accuracy / confusion: 97.27% / [[8333, 152, 56], [174, 7237, 79], [43, 72, 4942]], 
* Val   accuracy / confusion: 94.77% / [[1029, 37, 3], [29, 893, 16], [21, 32, 580]]

------------------------------ Epoch 042 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.020631
- Iter 262 / 659, Loss: 0.010417
- Iter 393 / 659, Loss: 0.053283
- Iter 524 / 659, Loss: 0.102391
- Iter 655 / 659, Loss: 0.089663

* Train accuracy / confusion: 97.39% / [[8331, 165, 46], [174, 7257, 60], [50, 55, 4950]], 
* Val   accuracy / confusion: 95.42% / [[1043, 15, 11], [44, 863, 31], [9, 11, 613]]

------------------------------ Epoch 043 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.129264
- Iter 262 / 659, Loss: 0.045615
- Iter 393 / 659, Loss: 0.004701
- Iter 524 / 659, Loss: 0.148597
- Iter 655 / 659, Loss: 0.010810

* Train accuracy / confusion: 97.52% / [[8365, 129, 52], [153, 7267, 68], [53, 68, 4933]], 
* Val   accuracy / confusion: 96.21% / [[1047, 11, 11], [34, 888, 16], [13, 15, 605]]

------------------------------ Epoch 044 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.022898
- Iter 262 / 659, Loss: 0.020602
- Iter 393 / 659, Loss: 0.089418
- Iter 524 / 659, Loss: 0.049078
- Iter 655 / 659, Loss: 0.121087

* Train accuracy / confusion: 97.64% / [[8389, 114, 42], [157, 7255, 75], [37, 72, 4947]], 
* Val   accuracy / confusion: 95.42% / [[1036, 30, 3], [38, 891, 9], [18, 23, 592]]

------------------------------ Epoch 045 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.007643
- Iter 262 / 659, Loss: 0.024442
- Iter 393 / 659, Loss: 0.004616
- Iter 524 / 659, Loss: 0.104757
- Iter 655 / 659, Loss: 0.134845

* Train accuracy / confusion: 97.52% / [[8344, 152, 46], [150, 7270, 72], [44, 58, 4952]], 
* Val   accuracy / confusion: 96.14% / [[1045, 21, 3], [30, 904, 4], [21, 23, 589]]

------------------------------ Epoch 046 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.110746
- Iter 262 / 659, Loss: 0.005637
- Iter 393 / 659, Loss: 0.049125
- Iter 524 / 659, Loss: 0.040941
- Iter 655 / 659, Loss: 0.036409

* Train accuracy / confusion: 97.61% / [[8358, 136, 47], [136, 7292, 67], [48, 71, 4933]], 
* Val   accuracy / confusion: 96.10% / [[1056, 10, 3], [44, 888, 6], [16, 24, 593]]

------------------------------ Epoch 047 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.036181
- Iter 262 / 659, Loss: 0.097492
- Iter 393 / 659, Loss: 0.031589
- Iter 524 / 659, Loss: 0.046595
- Iter 655 / 659, Loss: 0.026700

* Train accuracy / confusion: 97.84% / [[8369, 126, 50], [127, 7312, 53], [46, 54, 4951]], 
* Val   accuracy / confusion: 95.42% / [[1027, 29, 13], [25, 896, 17], [5, 32, 596]]

------------------------------ Epoch 048 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.030430
- Iter 262 / 659, Loss: 0.006493
- Iter 393 / 659, Loss: 0.044731
- Iter 524 / 659, Loss: 0.012781
- Iter 655 / 659, Loss: 0.007181

* Train accuracy / confusion: 97.84% / [[8385, 118, 39], [125, 7296, 70], [40, 63, 4952]], 
* Val   accuracy / confusion: 96.86% / [[1050, 12, 7], [28, 901, 9], [13, 14, 606]]

------------------------------ Epoch 049 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.339833
- Iter 262 / 659, Loss: 0.033589
- Iter 393 / 659, Loss: 0.031243
- Iter 524 / 659, Loss: 0.251266
- Iter 655 / 659, Loss: 0.009572

* Train accuracy / confusion: 97.94% / [[8394, 109, 41], [115, 7306, 69], [44, 57, 4953]], 
* Val   accuracy / confusion: 96.21% / [[1043, 20, 6], [41, 888, 9], [12, 12, 609]]

------------------------------ Epoch 050 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.024892
- Iter 262 / 659, Loss: 0.003851
- Iter 393 / 659, Loss: 0.097352
- Iter 524 / 659, Loss: 0.113760
- Iter 655 / 659, Loss: 0.055499

* Train accuracy / confusion: 98.16% / [[8404, 98, 43], [114, 7331, 44], [35, 54, 4965]], 
* Val   accuracy / confusion: 95.68% / [[1015, 51, 3], [8, 926, 4], [11, 37, 585]]

------------------------------ Epoch 051 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.016763
- Iter 262 / 659, Loss: 0.004780
- Iter 393 / 659, Loss: 0.141279
- Iter 524 / 659, Loss: 0.008692
- Iter 655 / 659, Loss: 0.060415

* Train accuracy / confusion: 97.76% / [[8387, 119, 38], [152, 7272, 65], [38, 61, 4956]], 
* Val   accuracy / confusion: 96.36% / [[1028, 13, 28], [18, 898, 22], [2, 13, 618]]

------------------------------ Epoch 052 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.006083
- Iter 262 / 659, Loss: 0.003416
- Iter 393 / 659, Loss: 0.009850
- Iter 524 / 659, Loss: 0.141432
- Iter 655 / 659, Loss: 0.008263

* Train accuracy / confusion: 98.21% / [[8418, 85, 38], [99, 7333, 61], [40, 55, 4959]], 
* Val   accuracy / confusion: 95.53% / [[1040, 14, 15], [35, 863, 40], [11, 3, 619]]

------------------------------ Epoch 053 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.009232
- Iter 262 / 659, Loss: 0.008041
- Iter 393 / 659, Loss: 0.129590
- Iter 524 / 659, Loss: 0.004659
- Iter 655 / 659, Loss: 0.055143

* Train accuracy / confusion: 98.35% / [[8416, 90, 34], [100, 7341, 50], [36, 39, 4982]], 
* Val   accuracy / confusion: 96.40% / [[1038, 26, 5], [24, 903, 11], [6, 23, 604]]

------------------------------ Epoch 054 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.003969
- Iter 262 / 659, Loss: 0.102006
- Iter 393 / 659, Loss: 0.016919
- Iter 524 / 659, Loss: 0.020327
- Iter 655 / 659, Loss: 0.018004

* Train accuracy / confusion: 98.35% / [[8422, 90, 29], [87, 7350, 54], [39, 50, 4967]], 
* Val   accuracy / confusion: 96.86% / [[1054, 10, 5], [32, 894, 12], [10, 14, 609]]

------------------------------ Epoch 055 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.035043
- Iter 262 / 659, Loss: 0.040483
- Iter 393 / 659, Loss: 0.060692
- Iter 524 / 659, Loss: 0.003913
- Iter 655 / 659, Loss: 0.011661

* Train accuracy / confusion: 98.49% / [[8437, 76, 30], [95, 7358, 40], [30, 47, 4975]], 
* Val   accuracy / confusion: 96.86% / [[1056, 6, 7], [36, 887, 15], [9, 10, 614]]

------------------------------ Epoch 056 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.080432
- Iter 262 / 659, Loss: 0.008207
- Iter 393 / 659, Loss: 0.002960
- Iter 524 / 659, Loss: 0.014243
- Iter 655 / 659, Loss: 0.006021

* Train accuracy / confusion: 98.16% / [[8388, 115, 40], [116, 7328, 45], [36, 37, 4983]], 
* Val   accuracy / confusion: 96.70% / [[1037, 26, 6], [18, 911, 9], [7, 21, 605]]

------------------------------ Epoch 057 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.116149
- Iter 262 / 659, Loss: 0.009400
- Iter 393 / 659, Loss: 0.004662
- Iter 524 / 659, Loss: 0.008270
- Iter 655 / 659, Loss: 0.019433

* Train accuracy / confusion: 98.48% / [[8428, 84, 29], [90, 7353, 46], [29, 42, 4987]], 
* Val   accuracy / confusion: 96.29% / [[1047, 20, 2], [24, 899, 15], [14, 23, 596]]

------------------------------ Epoch 058 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.016479
- Iter 262 / 659, Loss: 0.005724
- Iter 393 / 659, Loss: 0.009080
- Iter 524 / 659, Loss: 0.109743
- Iter 655 / 659, Loss: 0.003292

* Train accuracy / confusion: 98.58% / [[8429, 82, 27], [75, 7373, 47], [24, 44, 4987]], 
* Val   accuracy / confusion: 96.59% / [[1044, 12, 13], [30, 891, 17], [10, 8, 615]]

------------------------------ Epoch 059 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.007046
- Iter 262 / 659, Loss: 0.002067
- Iter 393 / 659, Loss: 0.020904
- Iter 524 / 659, Loss: 0.005879
- Iter 655 / 659, Loss: 0.000744

* Train accuracy / confusion: 98.64% / [[8441, 71, 32], [81, 7378, 31], [38, 33, 4983]], 
* Val   accuracy / confusion: 96.82% / [[1060, 1, 8], [41, 879, 18], [11, 5, 617]]

------------------------------ Epoch 060 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.001732
- Iter 262 / 659, Loss: 0.063168
- Iter 393 / 659, Loss: 0.082241
- Iter 524 / 659, Loss: 0.007980
- Iter 655 / 659, Loss: 0.001760

* Train accuracy / confusion: 98.37% / [[8429, 88, 27], [106, 7338, 45], [33, 44, 4978]], 
* Val   accuracy / confusion: 97.23% / [[1051, 12, 6], [19, 911, 8], [10, 18, 605]]

------------------------------ Epoch 061 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.045412
- Iter 262 / 659, Loss: 0.006120
- Iter 393 / 659, Loss: 0.002730
- Iter 524 / 659, Loss: 0.000931
- Iter 655 / 659, Loss: 0.065863

* Train accuracy / confusion: 98.69% / [[8440, 73, 30], [76, 7382, 32], [24, 42, 4989]], 
* Val   accuracy / confusion: 97.27% / [[1058, 9, 2], [23, 900, 15], [14, 9, 610]]

------------------------------ Epoch 062 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.003129
- Iter 262 / 659, Loss: 0.049211
- Iter 393 / 659, Loss: 0.078614
- Iter 524 / 659, Loss: 0.027362
- Iter 655 / 659, Loss: 0.126358

* Train accuracy / confusion: 98.63% / [[8441, 65, 35], [68, 7379, 43], [39, 39, 4979]], 
* Val   accuracy / confusion: 95.83% / [[1062, 4, 3], [52, 875, 11], [29, 11, 593]]

------------------------------ Epoch 063 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.005435
- Iter 262 / 659, Loss: 0.002918
- Iter 393 / 659, Loss: 0.014603
- Iter 524 / 659, Loss: 0.006094
- Iter 655 / 659, Loss: 0.006345

* Train accuracy / confusion: 98.59% / [[8435, 81, 27], [89, 7362, 39], [28, 33, 4994]], 
* Val   accuracy / confusion: 97.77% / [[1053, 14, 2], [18, 915, 5], [6, 14, 613]]

------------------------------ Epoch 064 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.007168
- Iter 262 / 659, Loss: 0.053425
- Iter 393 / 659, Loss: 0.060259
- Iter 524 / 659, Loss: 0.033567
- Iter 655 / 659, Loss: 0.013445

* Train accuracy / confusion: 98.58% / [[8436, 74, 37], [92, 7362, 35], [34, 28, 4990]], 
* Val   accuracy / confusion: 97.27% / [[1041, 18, 10], [19, 905, 14], [5, 6, 622]]

------------------------------ Epoch 065 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.050279
- Iter 262 / 659, Loss: 0.007600
- Iter 393 / 659, Loss: 0.016048
- Iter 524 / 659, Loss: 0.002963
- Iter 655 / 659, Loss: 0.002173

* Train accuracy / confusion: 98.84% / [[8438, 73, 28], [69, 7397, 24], [25, 25, 5009]], 
* Val   accuracy / confusion: 97.27% / [[1051, 14, 4], [22, 905, 11], [6, 15, 612]]

------------------------------ Epoch 066 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.002238
- Iter 262 / 659, Loss: 0.001829
- Iter 393 / 659, Loss: 0.046855
- Iter 524 / 659, Loss: 0.204337
- Iter 655 / 659, Loss: 0.010662

* Train accuracy / confusion: 98.61% / [[8440, 74, 36], [69, 7377, 41], [40, 33, 4978]], 
* Val   accuracy / confusion: 97.27% / [[1050, 14, 5], [25, 904, 9], [11, 8, 614]]

------------------------------ Epoch 067 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.017416
- Iter 262 / 659, Loss: 0.013357
- Iter 393 / 659, Loss: 0.030891
- Iter 524 / 659, Loss: 0.110937
- Iter 655 / 659, Loss: 0.044019

* Train accuracy / confusion: 98.68% / [[8448, 69, 25], [66, 7380, 45], [32, 41, 4982]], 
* Val   accuracy / confusion: 97.31% / [[1030, 28, 11], [11, 919, 8], [3, 10, 620]]

------------------------------ Epoch 068 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.002333
- Iter 262 / 659, Loss: 0.031482
- Iter 393 / 659, Loss: 0.007384
- Iter 524 / 659, Loss: 0.056692
- Iter 655 / 659, Loss: 0.181711

* Train accuracy / confusion: 98.85% / [[8458, 64, 21], [70, 7388, 32], [25, 31, 4999]], 
* Val   accuracy / confusion: 97.01% / [[1051, 13, 5], [32, 902, 4], [11, 14, 608]]

------------------------------ Epoch 069 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.007595
- Iter 262 / 659, Loss: 0.026506
- Iter 393 / 659, Loss: 0.206569
- Iter 524 / 659, Loss: 0.012553
- Iter 655 / 659, Loss: 0.022398

* Train accuracy / confusion: 98.86% / [[8450, 69, 24], [78, 7393, 21], [23, 25, 5005]], 
* Val   accuracy / confusion: 97.12% / [[1059, 6, 4], [47, 885, 6], [6, 7, 620]]

------------------------------ Epoch 070 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.006747
- Iter 262 / 659, Loss: 0.000351
- Iter 393 / 659, Loss: 0.013890
- Iter 524 / 659, Loss: 0.031292
- Iter 655 / 659, Loss: 0.052359

* Train accuracy / confusion: 99.10% / [[8480, 48, 17], [51, 7408, 29], [20, 25, 5010]], 
* Val   accuracy / confusion: 96.59% / [[1053, 8, 8], [40, 887, 11], [11, 12, 610]]

------------------------------ Epoch 071 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.007470
- Iter 262 / 659, Loss: 0.011276
- Iter 393 / 659, Loss: 0.000970
- Iter 524 / 659, Loss: 0.170152
- Iter 655 / 659, Loss: 0.101994

* Train accuracy / confusion: 98.80% / [[8452, 67, 24], [74, 7380, 38], [18, 33, 5002]], 
* Val   accuracy / confusion: 96.70% / [[1046, 17, 6], [19, 912, 7], [18, 20, 595]]

------------------------------ Epoch 072 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.001468
- Iter 262 / 659, Loss: 0.019962
- Iter 393 / 659, Loss: 0.091885
- Iter 524 / 659, Loss: 0.003522
- Iter 655 / 659, Loss: 0.011923

* Train accuracy / confusion: 98.85% / [[8461, 53, 33], [64, 7397, 26], [30, 36, 4988]], 
* Val   accuracy / confusion: 97.23% / [[1030, 26, 13], [14, 920, 4], [3, 13, 617]]

------------------------------ Epoch 073 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.009079
- Iter 262 / 659, Loss: 0.003852
- Iter 393 / 659, Loss: 0.000365
- Iter 524 / 659, Loss: 0.026684
- Iter 655 / 659, Loss: 0.001810

* Train accuracy / confusion: 98.99% / [[8464, 61, 21], [60, 7395, 31], [19, 20, 5017]], 
* Val   accuracy / confusion: 97.08% / [[1050, 12, 7], [24, 907, 7], [16, 11, 606]]

------------------------------ Epoch 074 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.019830
- Iter 262 / 659, Loss: 0.001344
- Iter 393 / 659, Loss: 0.122605
- Iter 524 / 659, Loss: 0.003732
- Iter 655 / 659, Loss: 0.000873

* Train accuracy / confusion: 98.95% / [[8463, 64, 15], [64, 7398, 29], [23, 26, 5006]], 
* Val   accuracy / confusion: 96.93% / [[1053, 11, 5], [37, 889, 12], [10, 6, 617]]

------------------------------ Epoch 075 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.004949
- Iter 262 / 659, Loss: 0.006491
- Iter 393 / 659, Loss: 0.008146
- Iter 524 / 659, Loss: 0.015025
- Iter 655 / 659, Loss: 0.002193

* Train accuracy / confusion: 99.03% / [[8466, 46, 28], [59, 7409, 21], [26, 25, 5008]], 
* Val   accuracy / confusion: 97.46% / [[1053, 6, 10], [21, 912, 5], [17, 8, 608]]

------------------------------ Epoch 076 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.019614
- Iter 262 / 659, Loss: 0.000631
- Iter 393 / 659, Loss: 0.000422
- Iter 524 / 659, Loss: 0.097597
- Iter 655 / 659, Loss: 0.003304

* Train accuracy / confusion: 98.76% / [[8448, 66, 32], [63, 7391, 32], [36, 32, 4988]], 
* Val   accuracy / confusion: 97.08% / [[1059, 7, 3], [32, 896, 10], [15, 10, 608]]

------------------------------ Epoch 077 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.004596
- Iter 262 / 659, Loss: 0.002516
- Iter 393 / 659, Loss: 0.001838
- Iter 524 / 659, Loss: 0.000456
- Iter 655 / 659, Loss: 0.003376

* Train accuracy / confusion: 99.05% / [[8466, 52, 23], [54, 7407, 30], [18, 24, 5014]], 
* Val   accuracy / confusion: 97.99% / [[1062, 6, 1], [25, 910, 3], [11, 7, 615]]

------------------------------ Epoch 078 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.007686
- Iter 262 / 659, Loss: 0.089044
- Iter 393 / 659, Loss: 0.032430
- Iter 524 / 659, Loss: 0.163156
- Iter 655 / 659, Loss: 0.097420

* Train accuracy / confusion: 99.09% / [[8466, 52, 22], [57, 7411, 22], [23, 16, 5019]], 
* Val   accuracy / confusion: 96.63% / [[1029, 35, 5], [12, 919, 7], [8, 22, 603]]

------------------------------ Epoch 079 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.012888
- Iter 262 / 659, Loss: 0.029915
- Iter 393 / 659, Loss: 0.011424
- Iter 524 / 659, Loss: 0.006399
- Iter 655 / 659, Loss: 0.051414

* Train accuracy / confusion: 99.01% / [[8461, 55, 26], [60, 7410, 22], [25, 20, 5009]], 
* Val   accuracy / confusion: 97.95% / [[1052, 11, 6], [14, 916, 8], [6, 9, 618]]

------------------------------ Epoch 080 / 200, Learning rate: 6.51e-04 ------------------------------
- Iter 131 / 659, Loss: 0.008910
- Iter 262 / 659, Loss: 0.004905
- Iter 393 / 659, Loss: 0.136057
- Iter 524 / 659, Loss: 0.014954
- Iter 655 / 659, Loss: 0.014575

* Train accuracy / confusion: 99.17% / [[8490, 34, 19], [41, 7417, 30], [20, 30, 5007]], 
* Val   accuracy / confusion: 97.54% / [[1046, 17, 6], [16, 914, 8], [11, 7, 615]]

------------------------------ Epoch 081 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.008095
- Iter 262 / 659, Loss: 0.001504
- Iter 393 / 659, Loss: 0.007243
- Iter 524 / 659, Loss: 0.010173
- Iter 655 / 659, Loss: 0.000587

* Train accuracy / confusion: 99.46% / [[8502, 25, 15], [36, 7437, 19], [7, 11, 5036]], 
* Val   accuracy / confusion: 98.37% / [[1057, 6, 6], [16, 919, 3], [7, 5, 621]]

------------------------------ Epoch 082 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000795
- Iter 262 / 659, Loss: 0.005069
- Iter 393 / 659, Loss: 0.000258
- Iter 524 / 659, Loss: 0.004969
- Iter 655 / 659, Loss: 0.000137

* Train accuracy / confusion: 99.63% / [[8510, 24, 8], [26, 7458, 7], [8, 4, 5043]], 
* Val   accuracy / confusion: 97.92% / [[1057, 4, 8], [17, 913, 8], [12, 6, 615]]

------------------------------ Epoch 083 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.004529
- Iter 262 / 659, Loss: 0.004892
- Iter 393 / 659, Loss: 0.003107
- Iter 524 / 659, Loss: 0.004175
- Iter 655 / 659, Loss: 0.000205

* Train accuracy / confusion: 99.74% / [[8528, 11, 6], [19, 7470, 3], [8, 7, 5036]], 
* Val   accuracy / confusion: 98.56% / [[1062, 4, 3], [16, 918, 4], [5, 6, 622]]

------------------------------ Epoch 084 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.002347
- Iter 262 / 659, Loss: 0.000146
- Iter 393 / 659, Loss: 0.002100
- Iter 524 / 659, Loss: 0.043149
- Iter 655 / 659, Loss: 0.000158

* Train accuracy / confusion: 99.81% / [[8526, 7, 10], [11, 7474, 4], [7, 2, 5047]], 
* Val   accuracy / confusion: 98.07% / [[1059, 9, 1], [19, 914, 5], [10, 7, 616]]

------------------------------ Epoch 085 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.006016
- Iter 262 / 659, Loss: 0.001858
- Iter 393 / 659, Loss: 0.000610
- Iter 524 / 659, Loss: 0.000357
- Iter 655 / 659, Loss: 0.000921

* Train accuracy / confusion: 99.84% / [[8524, 10, 6], [7, 7487, 2], [5, 3, 5044]], 
* Val   accuracy / confusion: 98.37% / [[1059, 6, 4], [11, 922, 5], [11, 6, 616]]

------------------------------ Epoch 086 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.003742
- Iter 262 / 659, Loss: 0.001647
- Iter 393 / 659, Loss: 0.000451
- Iter 524 / 659, Loss: 0.007948
- Iter 655 / 659, Loss: 0.000405

* Train accuracy / confusion: 99.86% / [[8533, 5, 2], [12, 7480, 2], [4, 4, 5046]], 
* Val   accuracy / confusion: 98.56% / [[1062, 3, 4], [11, 921, 6], [8, 6, 619]]

------------------------------ Epoch 087 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000178
- Iter 262 / 659, Loss: 0.000346
- Iter 393 / 659, Loss: 0.039143
- Iter 524 / 659, Loss: 0.005459
- Iter 655 / 659, Loss: 0.000872

* Train accuracy / confusion: 99.83% / [[8530, 13, 1], [10, 7473, 5], [3, 3, 5050]], 
* Val   accuracy / confusion: 98.56% / [[1066, 3, 0], [15, 920, 3], [11, 6, 616]]

------------------------------ Epoch 088 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.001032
- Iter 262 / 659, Loss: 0.052469
- Iter 393 / 659, Loss: 0.001677
- Iter 524 / 659, Loss: 0.004725
- Iter 655 / 659, Loss: 0.000305

* Train accuracy / confusion: 99.82% / [[8521, 9, 7], [11, 7481, 3], [3, 4, 5049]], 
* Val   accuracy / confusion: 98.71% / [[1062, 5, 2], [10, 925, 3], [8, 6, 619]]

------------------------------ Epoch 089 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.001446
- Iter 262 / 659, Loss: 0.000094
- Iter 393 / 659, Loss: 0.000450
- Iter 524 / 659, Loss: 0.001426
- Iter 655 / 659, Loss: 0.000243

* Train accuracy / confusion: 99.87% / [[8527, 10, 3], [5, 7489, 0], [6, 4, 5044]], 
* Val   accuracy / confusion: 98.33% / [[1060, 6, 3], [14, 920, 4], [9, 8, 616]]

------------------------------ Epoch 090 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000343
- Iter 262 / 659, Loss: 0.021390
- Iter 393 / 659, Loss: 0.000125
- Iter 524 / 659, Loss: 0.004335
- Iter 655 / 659, Loss: 0.000509

* Train accuracy / confusion: 99.80% / [[8526, 12, 5], [10, 7470, 6], [5, 4, 5050]], 
* Val   accuracy / confusion: 98.22% / [[1063, 3, 3], [15, 918, 5], [13, 8, 612]]

------------------------------ Epoch 091 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000859
- Iter 262 / 659, Loss: 0.050406
- Iter 393 / 659, Loss: 0.000038
- Iter 524 / 659, Loss: 0.012091
- Iter 655 / 659, Loss: 0.006991

* Train accuracy / confusion: 99.86% / [[8530, 9, 1], [12, 7479, 1], [4, 3, 5049]], 
* Val   accuracy / confusion: 98.75% / [[1063, 4, 2], [10, 923, 5], [7, 5, 621]]

------------------------------ Epoch 092 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000526
- Iter 262 / 659, Loss: 0.002026
- Iter 393 / 659, Loss: 0.001250
- Iter 524 / 659, Loss: 0.010150
- Iter 655 / 659, Loss: 0.000157

* Train accuracy / confusion: 99.84% / [[8532, 9, 3], [9, 7474, 6], [0, 6, 5049]], 
* Val   accuracy / confusion: 98.75% / [[1063, 3, 3], [11, 923, 4], [10, 2, 621]]

------------------------------ Epoch 093 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000031
- Iter 262 / 659, Loss: 0.000275
- Iter 393 / 659, Loss: 0.130362
- Iter 524 / 659, Loss: 0.001338
- Iter 655 / 659, Loss: 0.000175

* Train accuracy / confusion: 99.85% / [[8535, 10, 1], [10, 7477, 4], [2, 5, 5044]], 
* Val   accuracy / confusion: 98.67% / [[1064, 5, 0], [14, 920, 4], [7, 5, 621]]

------------------------------ Epoch 094 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000230
- Iter 262 / 659, Loss: 0.006242
- Iter 393 / 659, Loss: 0.000089
- Iter 524 / 659, Loss: 0.000678
- Iter 655 / 659, Loss: 0.000784

* Train accuracy / confusion: 99.91% / [[8535, 5, 3], [6, 7485, 2], [1, 2, 5049]], 
* Val   accuracy / confusion: 98.71% / [[1061, 5, 3], [15, 919, 4], [3, 4, 626]]

------------------------------ Epoch 095 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.006960
- Iter 262 / 659, Loss: 0.002507
- Iter 393 / 659, Loss: 0.001279
- Iter 524 / 659, Loss: 0.000245
- Iter 655 / 659, Loss: 0.000067

* Train accuracy / confusion: 99.90% / [[8533, 9, 3], [6, 7484, 0], [1, 2, 5050]], 
* Val   accuracy / confusion: 98.33% / [[1057, 7, 5], [11, 922, 5], [10, 6, 617]]

------------------------------ Epoch 096 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.003765
- Iter 262 / 659, Loss: 0.000062
- Iter 393 / 659, Loss: 0.000126
- Iter 524 / 659, Loss: 0.005088
- Iter 655 / 659, Loss: 0.000102

* Train accuracy / confusion: 99.88% / [[8535, 7, 3], [9, 7480, 1], [4, 1, 5048]], 
* Val   accuracy / confusion: 98.64% / [[1058, 8, 3], [9, 925, 4], [6, 6, 621]]

------------------------------ Epoch 097 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000022
- Iter 262 / 659, Loss: 0.000147
- Iter 393 / 659, Loss: 0.000244
- Iter 524 / 659, Loss: 0.000993
- Iter 655 / 659, Loss: 0.000398

* Train accuracy / confusion: 99.91% / [[8532, 6, 0], [6, 7483, 3], [3, 2, 5053]], 
* Val   accuracy / confusion: 98.56% / [[1063, 4, 2], [17, 918, 3], [7, 5, 621]]

------------------------------ Epoch 098 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.001906
- Iter 262 / 659, Loss: 0.000702
- Iter 393 / 659, Loss: 0.000081
- Iter 524 / 659, Loss: 0.011206
- Iter 655 / 659, Loss: 0.000110

* Train accuracy / confusion: 99.92% / [[8537, 4, 3], [5, 7483, 3], [1, 1, 5051]], 
* Val   accuracy / confusion: 98.56% / [[1056, 10, 3], [8, 928, 2], [6, 9, 618]]

------------------------------ Epoch 099 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000420
- Iter 262 / 659, Loss: 0.000751
- Iter 393 / 659, Loss: 0.000174
- Iter 524 / 659, Loss: 0.000729
- Iter 655 / 659, Loss: 0.000400

* Train accuracy / confusion: 99.95% / [[8541, 6, 0], [2, 7485, 0], [2, 0, 5052]], 
* Val   accuracy / confusion: 98.64% / [[1063, 6, 0], [9, 926, 3], [7, 11, 615]]

------------------------------ Epoch 100 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000182
- Iter 262 / 659, Loss: 0.003715
- Iter 393 / 659, Loss: 0.001988
- Iter 524 / 659, Loss: 0.000049
- Iter 655 / 659, Loss: 0.000018

* Train accuracy / confusion: 99.88% / [[8535, 5, 3], [7, 7481, 3], [6, 2, 5046]], 
* Val   accuracy / confusion: 98.45% / [[1064, 4, 1], [17, 917, 4], [9, 6, 618]]

------------------------------ Epoch 101 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.001633
- Iter 262 / 659, Loss: 0.000699
- Iter 393 / 659, Loss: 0.000067
- Iter 524 / 659, Loss: 0.008873
- Iter 655 / 659, Loss: 0.000299

* Train accuracy / confusion: 99.92% / [[8544, 4, 1], [7, 7481, 0], [3, 2, 5046]], 
* Val   accuracy / confusion: 98.83% / [[1065, 3, 1], [18, 920, 0], [6, 3, 624]]

------------------------------ Epoch 102 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.003791
- Iter 262 / 659, Loss: 0.000362
- Iter 393 / 659, Loss: 0.000456
- Iter 524 / 659, Loss: 0.000352
- Iter 655 / 659, Loss: 0.000180

* Train accuracy / confusion: 99.87% / [[8530, 6, 3], [6, 7483, 4], [6, 3, 5047]], 
* Val   accuracy / confusion: 98.33% / [[1059, 7, 3], [13, 923, 2], [10, 9, 614]]

------------------------------ Epoch 103 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000142
- Iter 262 / 659, Loss: 0.000219
- Iter 393 / 659, Loss: 0.002910
- Iter 524 / 659, Loss: 0.000714
- Iter 655 / 659, Loss: 0.001003

* Train accuracy / confusion: 99.94% / [[8541, 1, 1], [7, 7481, 0], [2, 1, 5054]], 
* Val   accuracy / confusion: 98.64% / [[1061, 5, 3], [12, 923, 3], [9, 4, 620]]

------------------------------ Epoch 104 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.001207
- Iter 262 / 659, Loss: 0.000319
- Iter 393 / 659, Loss: 0.000027
- Iter 524 / 659, Loss: 0.017531
- Iter 655 / 659, Loss: 0.000325

* Train accuracy / confusion: 99.88% / [[8528, 12, 2], [8, 7485, 1], [1, 2, 5049]], 
* Val   accuracy / confusion: 98.37% / [[1060, 6, 3], [17, 916, 5], [8, 4, 621]]

------------------------------ Epoch 105 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000524
- Iter 262 / 659, Loss: 0.000108
- Iter 393 / 659, Loss: 0.000778
- Iter 524 / 659, Loss: 0.000254
- Iter 655 / 659, Loss: 0.000228

* Train accuracy / confusion: 99.91% / [[8545, 2, 1], [8, 7477, 3], [4, 1, 5047]], 
* Val   accuracy / confusion: 98.94% / [[1065, 4, 0], [12, 922, 4], [5, 3, 625]]

------------------------------ Epoch 106 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000825
- Iter 262 / 659, Loss: 0.000493
- Iter 393 / 659, Loss: 0.000212
- Iter 524 / 659, Loss: 0.000009
- Iter 655 / 659, Loss: 0.002260

* Train accuracy / confusion: 99.91% / [[8539, 5, 1], [6, 7482, 3], [3, 1, 5048]], 
* Val   accuracy / confusion: 98.52% / [[1062, 6, 1], [13, 922, 3], [8, 8, 617]]

------------------------------ Epoch 107 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000895
- Iter 262 / 659, Loss: 0.008687
- Iter 393 / 659, Loss: 0.000026
- Iter 524 / 659, Loss: 0.000033
- Iter 655 / 659, Loss: 0.000514

* Train accuracy / confusion: 99.90% / [[8532, 6, 3], [7, 7486, 2], [2, 1, 5049]], 
* Val   accuracy / confusion: 98.64% / [[1064, 1, 4], [15, 920, 3], [7, 6, 620]]

------------------------------ Epoch 108 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000772
- Iter 262 / 659, Loss: 0.000189
- Iter 393 / 659, Loss: 0.001736
- Iter 524 / 659, Loss: 0.000364
- Iter 655 / 659, Loss: 0.000169

* Train accuracy / confusion: 99.95% / [[8540, 4, 1], [2, 7483, 3], [1, 0, 5054]], 
* Val   accuracy / confusion: 98.64% / [[1063, 6, 0], [9, 925, 4], [10, 7, 616]]

------------------------------ Epoch 109 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000546
- Iter 262 / 659, Loss: 0.003656
- Iter 393 / 659, Loss: 0.008815
- Iter 524 / 659, Loss: 0.000103
- Iter 655 / 659, Loss: 0.003048

* Train accuracy / confusion: 99.93% / [[8536, 4, 3], [5, 7486, 1], [1, 0, 5052]], 
* Val   accuracy / confusion: 98.52% / [[1066, 1, 2], [17, 919, 2], [11, 6, 616]]

------------------------------ Epoch 110 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000248
- Iter 262 / 659, Loss: 0.000040
- Iter 393 / 659, Loss: 0.002449
- Iter 524 / 659, Loss: 0.008007
- Iter 655 / 659, Loss: 0.000032

* Train accuracy / confusion: 99.92% / [[8534, 6, 5], [2, 7490, 1], [2, 1, 5047]], 
* Val   accuracy / confusion: 98.45% / [[1066, 3, 0], [21, 914, 3], [8, 6, 619]]

------------------------------ Epoch 111 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000130
- Iter 262 / 659, Loss: 0.000030
- Iter 393 / 659, Loss: 0.007409
- Iter 524 / 659, Loss: 0.000331
- Iter 655 / 659, Loss: 0.000180

* Train accuracy / confusion: 99.92% / [[8542, 1, 1], [6, 7486, 0], [6, 3, 5043]], 
* Val   accuracy / confusion: 98.71% / [[1061, 3, 5], [13, 918, 7], [4, 2, 627]]

------------------------------ Epoch 112 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000356
- Iter 262 / 659, Loss: 0.010753
- Iter 393 / 659, Loss: 0.000801
- Iter 524 / 659, Loss: 0.001307
- Iter 655 / 659, Loss: 0.000400

* Train accuracy / confusion: 99.94% / [[8535, 7, 2], [2, 7487, 0], [1, 1, 5053]], 
* Val   accuracy / confusion: 98.48% / [[1061, 7, 1], [14, 920, 4], [8, 6, 619]]

------------------------------ Epoch 113 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000083
- Iter 262 / 659, Loss: 0.000063
- Iter 393 / 659, Loss: 0.000171
- Iter 524 / 659, Loss: 0.000118
- Iter 655 / 659, Loss: 0.000075

* Train accuracy / confusion: 99.92% / [[8532, 8, 4], [2, 7489, 0], [3, 0, 5050]], 
* Val   accuracy / confusion: 98.67% / [[1063, 5, 1], [14, 922, 2], [5, 8, 620]]

------------------------------ Epoch 114 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000038
- Iter 262 / 659, Loss: 0.000039
- Iter 393 / 659, Loss: 0.000240
- Iter 524 / 659, Loss: 0.001569
- Iter 655 / 659, Loss: 0.000165

* Train accuracy / confusion: 99.90% / [[8539, 2, 3], [6, 7485, 2], [4, 4, 5043]], 
* Val   accuracy / confusion: 98.67% / [[1059, 7, 3], [9, 925, 4], [5, 7, 621]]

------------------------------ Epoch 115 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000179
- Iter 262 / 659, Loss: 0.001501
- Iter 393 / 659, Loss: 0.000149
- Iter 524 / 659, Loss: 0.000154
- Iter 655 / 659, Loss: 0.000032

* Train accuracy / confusion: 99.95% / [[8539, 5, 2], [1, 7488, 0], [2, 1, 5050]], 
* Val   accuracy / confusion: 98.86% / [[1061, 6, 2], [13, 920, 5], [2, 2, 629]]

------------------------------ Epoch 116 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000028
- Iter 262 / 659, Loss: 0.000862
- Iter 393 / 659, Loss: 0.000061
- Iter 524 / 659, Loss: 0.000003
- Iter 655 / 659, Loss: 0.000215

* Train accuracy / confusion: 99.94% / [[8537, 6, 1], [3, 7485, 2], [0, 1, 5053]], 
* Val   accuracy / confusion: 98.60% / [[1061, 2, 6], [12, 923, 3], [7, 7, 619]]

------------------------------ Epoch 117 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000065
- Iter 262 / 659, Loss: 0.002470
- Iter 393 / 659, Loss: 0.000756
- Iter 524 / 659, Loss: 0.000040
- Iter 655 / 659, Loss: 0.000141

* Train accuracy / confusion: 99.91% / [[8540, 3, 3], [6, 7483, 2], [3, 2, 5046]], 
* Val   accuracy / confusion: 98.79% / [[1061, 7, 1], [8, 926, 4], [5, 7, 621]]

------------------------------ Epoch 118 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000090
- Iter 262 / 659, Loss: 0.000485
- Iter 393 / 659, Loss: 0.000003
- Iter 524 / 659, Loss: 0.000021
- Iter 655 / 659, Loss: 0.000016

* Train accuracy / confusion: 99.94% / [[8539, 3, 1], [3, 7489, 2], [2, 2, 5047]], 
* Val   accuracy / confusion: 98.83% / [[1061, 4, 4], [11, 926, 1], [6, 5, 622]]

------------------------------ Epoch 119 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000952
- Iter 262 / 659, Loss: 0.000063
- Iter 393 / 659, Loss: 0.004443
- Iter 524 / 659, Loss: 0.000050
- Iter 655 / 659, Loss: 0.000346

* Train accuracy / confusion: 99.93% / [[8535, 5, 3], [4, 7487, 1], [2, 0, 5051]], 
* Val   accuracy / confusion: 98.52% / [[1059, 7, 3], [9, 925, 4], [9, 7, 617]]

------------------------------ Epoch 120 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.001134
- Iter 262 / 659, Loss: 0.000084
- Iter 393 / 659, Loss: 0.000612
- Iter 524 / 659, Loss: 0.000739
- Iter 655 / 659, Loss: 0.000458

* Train accuracy / confusion: 99.94% / [[8537, 5, 1], [2, 7489, 1], [4, 0, 5049]], 
* Val   accuracy / confusion: 98.41% / [[1064, 4, 1], [20, 912, 6], [8, 3, 622]]

------------------------------ Epoch 121 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.003386
- Iter 262 / 659, Loss: 0.002845
- Iter 393 / 659, Loss: 0.000034
- Iter 524 / 659, Loss: 0.000047
- Iter 655 / 659, Loss: 0.001810

* Train accuracy / confusion: 99.93% / [[8538, 3, 4], [4, 7481, 1], [1, 2, 5054]], 
* Val   accuracy / confusion: 98.79% / [[1067, 1, 1], [13, 919, 6], [5, 6, 622]]

------------------------------ Epoch 122 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000052
- Iter 262 / 659, Loss: 0.000143
- Iter 393 / 659, Loss: 0.000218
- Iter 524 / 659, Loss: 0.000733
- Iter 655 / 659, Loss: 0.000109

* Train accuracy / confusion: 99.95% / [[8541, 3, 2], [4, 7485, 0], [2, 0, 5051]], 
* Val   accuracy / confusion: 98.52% / [[1066, 3, 0], [12, 922, 4], [9, 11, 613]]

------------------------------ Epoch 123 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.003464
- Iter 262 / 659, Loss: 0.000308
- Iter 393 / 659, Loss: 0.017305
- Iter 524 / 659, Loss: 0.000059
- Iter 655 / 659, Loss: 0.000007

* Train accuracy / confusion: 99.91% / [[8533, 4, 3], [5, 7482, 4], [3, 1, 5053]], 
* Val   accuracy / confusion: 98.52% / [[1066, 3, 0], [17, 918, 3], [11, 5, 617]]

------------------------------ Epoch 124 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000199
- Iter 262 / 659, Loss: 0.000027
- Iter 393 / 659, Loss: 0.000074
- Iter 524 / 659, Loss: 0.000385
- Iter 655 / 659, Loss: 0.000048

* Train accuracy / confusion: 99.93% / [[8536, 2, 4], [5, 7481, 2], [0, 1, 5057]], 
* Val   accuracy / confusion: 98.56% / [[1061, 6, 2], [7, 929, 2], [13, 8, 612]]

------------------------------ Epoch 125 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000020
- Iter 262 / 659, Loss: 0.000030
- Iter 393 / 659, Loss: 0.000071
- Iter 524 / 659, Loss: 0.000269
- Iter 655 / 659, Loss: 0.000796

* Train accuracy / confusion: 99.94% / [[8535, 7, 0], [4, 7484, 0], [1, 1, 5056]], 
* Val   accuracy / confusion: 98.45% / [[1065, 3, 1], [17, 913, 8], [8, 4, 621]]

------------------------------ Epoch 126 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.011429
- Iter 262 / 659, Loss: 0.000036
- Iter 393 / 659, Loss: 0.000262
- Iter 524 / 659, Loss: 0.000110
- Iter 655 / 659, Loss: 0.000013

* Train accuracy / confusion: 99.95% / [[8537, 2, 0], [4, 7487, 2], [0, 3, 5053]], 
* Val   accuracy / confusion: 98.56% / [[1063, 5, 1], [13, 921, 4], [9, 6, 618]]

------------------------------ Epoch 127 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000255
- Iter 262 / 659, Loss: 0.000013
- Iter 393 / 659, Loss: 0.000024
- Iter 524 / 659, Loss: 0.000146
- Iter 655 / 659, Loss: 0.000011

* Train accuracy / confusion: 99.96% / [[8541, 1, 0], [1, 7490, 2], [2, 2, 5049]], 
* Val   accuracy / confusion: 98.71% / [[1063, 4, 2], [16, 916, 6], [3, 3, 627]]

------------------------------ Epoch 128 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.002121
- Iter 262 / 659, Loss: 0.000039
- Iter 393 / 659, Loss: 0.000394
- Iter 524 / 659, Loss: 0.000060
- Iter 655 / 659, Loss: 0.000008

* Train accuracy / confusion: 99.96% / [[8542, 1, 2], [5, 7485, 0], [1, 0, 5052]], 
* Val   accuracy / confusion: 98.26% / [[1058, 9, 2], [17, 919, 2], [7, 9, 617]]

------------------------------ Epoch 129 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000303
- Iter 262 / 659, Loss: 0.000732
- Iter 393 / 659, Loss: 0.000447
- Iter 524 / 659, Loss: 0.000010
- Iter 655 / 659, Loss: 0.000542

* Train accuracy / confusion: 99.95% / [[8537, 2, 3], [1, 7486, 2], [1, 2, 5054]], 
* Val   accuracy / confusion: 98.67% / [[1065, 3, 1], [14, 920, 4], [8, 5, 620]]

------------------------------ Epoch 130 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.001832
- Iter 262 / 659, Loss: 0.000411
- Iter 393 / 659, Loss: 0.000711
- Iter 524 / 659, Loss: 0.000217
- Iter 655 / 659, Loss: 0.000385

* Train accuracy / confusion: 99.90% / [[8539, 4, 1], [9, 7479, 4], [1, 2, 5049]], 
* Val   accuracy / confusion: 98.83% / [[1062, 4, 3], [8, 926, 4], [8, 4, 621]]

------------------------------ Epoch 131 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000028
- Iter 262 / 659, Loss: 0.000147
- Iter 393 / 659, Loss: 0.000705
- Iter 524 / 659, Loss: 0.000173
- Iter 655 / 659, Loss: 0.009866

* Train accuracy / confusion: 99.96% / [[8544, 1, 0], [4, 7485, 2], [1, 1, 5050]], 
* Val   accuracy / confusion: 98.56% / [[1060, 6, 3], [11, 923, 4], [7, 7, 619]]

------------------------------ Epoch 132 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.001425
- Iter 262 / 659, Loss: 0.000013
- Iter 393 / 659, Loss: 0.000007
- Iter 524 / 659, Loss: 0.000088
- Iter 655 / 659, Loss: 0.000003

* Train accuracy / confusion: 99.95% / [[8538, 2, 4], [2, 7489, 0], [1, 2, 5050]], 
* Val   accuracy / confusion: 98.90% / [[1063, 5, 1], [11, 926, 1], [6, 5, 622]]

------------------------------ Epoch 133 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.001227
- Iter 262 / 659, Loss: 0.000431
- Iter 393 / 659, Loss: 0.000354
- Iter 524 / 659, Loss: 0.000201
- Iter 655 / 659, Loss: 0.000040

* Train accuracy / confusion: 99.95% / [[8544, 2, 0], [3, 7481, 1], [2, 2, 5053]], 
* Val   accuracy / confusion: 98.75% / [[1063, 3, 3], [14, 923, 1], [9, 3, 621]]

------------------------------ Epoch 134 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.002997
- Iter 262 / 659, Loss: 0.000110
- Iter 393 / 659, Loss: 0.000017
- Iter 524 / 659, Loss: 0.024448
- Iter 655 / 659, Loss: 0.000135

* Train accuracy / confusion: 99.96% / [[8539, 1, 2], [3, 7490, 1], [1, 0, 5051]], 
* Val   accuracy / confusion: 98.60% / [[1064, 5, 0], [16, 920, 2], [5, 9, 619]]

------------------------------ Epoch 135 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000133
- Iter 262 / 659, Loss: 0.000617
- Iter 393 / 659, Loss: 0.000015
- Iter 524 / 659, Loss: 0.000045
- Iter 655 / 659, Loss: 0.000065

* Train accuracy / confusion: 99.95% / [[8539, 1, 1], [3, 7486, 1], [1, 3, 5053]], 
* Val   accuracy / confusion: 98.71% / [[1064, 4, 1], [13, 922, 3], [8, 5, 620]]

------------------------------ Epoch 136 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000569
- Iter 262 / 659, Loss: 0.000041
- Iter 393 / 659, Loss: 0.000076
- Iter 524 / 659, Loss: 0.000607
- Iter 655 / 659, Loss: 0.000457

* Train accuracy / confusion: 99.95% / [[8536, 3, 3], [4, 7484, 0], [0, 0, 5058]], 
* Val   accuracy / confusion: 98.67% / [[1062, 6, 1], [11, 925, 2], [5, 10, 618]]

------------------------------ Epoch 137 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000004
- Iter 262 / 659, Loss: 0.000005
- Iter 393 / 659, Loss: 0.000040
- Iter 524 / 659, Loss: 0.000050
- Iter 655 / 659, Loss: 0.000045

* Train accuracy / confusion: 99.96% / [[8535, 3, 2], [1, 7493, 0], [1, 1, 5052]], 
* Val   accuracy / confusion: 98.94% / [[1066, 1, 2], [11, 927, 0], [11, 3, 619]]

------------------------------ Epoch 138 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000210
- Iter 262 / 659, Loss: 0.000143
- Iter 393 / 659, Loss: 0.000044
- Iter 524 / 659, Loss: 0.000028
- Iter 655 / 659, Loss: 0.000105

* Train accuracy / confusion: 99.96% / [[8544, 1, 1], [1, 7484, 2], [1, 3, 5051]], 
* Val   accuracy / confusion: 98.45% / [[1058, 8, 3], [13, 920, 5], [8, 4, 621]]

------------------------------ Epoch 139 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000031
- Iter 262 / 659, Loss: 0.000001
- Iter 393 / 659, Loss: 0.003732
- Iter 524 / 659, Loss: 0.000025
- Iter 655 / 659, Loss: 0.000031

* Train accuracy / confusion: 99.93% / [[8537, 3, 1], [7, 7481, 2], [1, 1, 5055]], 
* Val   accuracy / confusion: 98.90% / [[1063, 4, 2], [10, 925, 3], [3, 7, 623]]

------------------------------ Epoch 140 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000399
- Iter 262 / 659, Loss: 0.000496
- Iter 393 / 659, Loss: 0.000009
- Iter 524 / 659, Loss: 0.000352
- Iter 655 / 659, Loss: 0.000836

* Train accuracy / confusion: 99.95% / [[8540, 5, 0], [1, 7482, 2], [2, 1, 5055]], 
* Val   accuracy / confusion: 98.52% / [[1060, 8, 1], [16, 920, 2], [6, 6, 621]]

------------------------------ Epoch 141 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000586
- Iter 262 / 659, Loss: 0.000005
- Iter 393 / 659, Loss: 0.000215
- Iter 524 / 659, Loss: 0.000027
- Iter 655 / 659, Loss: 0.000220

* Train accuracy / confusion: 99.93% / [[8539, 2, 3], [4, 7483, 4], [1, 0, 5052]], 
* Val   accuracy / confusion: 98.67% / [[1065, 3, 1], [10, 925, 3], [12, 6, 615]]

------------------------------ Epoch 142 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.004669
- Iter 262 / 659, Loss: 0.000042
- Iter 393 / 659, Loss: 0.000086
- Iter 524 / 659, Loss: 0.000059
- Iter 655 / 659, Loss: 0.000139

* Train accuracy / confusion: 99.96% / [[8532, 4, 0], [4, 7491, 0], [0, 0, 5057]], 
* Val   accuracy / confusion: 99.05% / [[1065, 4, 0], [11, 925, 2], [6, 2, 625]]

------------------------------ Epoch 143 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000036
- Iter 262 / 659, Loss: 0.001381
- Iter 393 / 659, Loss: 0.008256
- Iter 524 / 659, Loss: 0.000202
- Iter 655 / 659, Loss: 0.000097

* Train accuracy / confusion: 99.98% / [[8539, 1, 1], [2, 7490, 0], [0, 1, 5054]], 
* Val   accuracy / confusion: 98.45% / [[1065, 3, 1], [16, 921, 1], [11, 9, 613]]

------------------------------ Epoch 144 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000036
- Iter 262 / 659, Loss: 0.000669
- Iter 393 / 659, Loss: 0.000349
- Iter 524 / 659, Loss: 0.000033
- Iter 655 / 659, Loss: 0.000082

* Train accuracy / confusion: 99.96% / [[8539, 2, 3], [2, 7487, 1], [0, 1, 5053]], 
* Val   accuracy / confusion: 98.56% / [[1063, 4, 2], [18, 916, 4], [7, 3, 623]]

------------------------------ Epoch 145 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000107
- Iter 262 / 659, Loss: 0.000097
- Iter 393 / 659, Loss: 0.000348
- Iter 524 / 659, Loss: 0.000034
- Iter 655 / 659, Loss: 0.000098

* Train accuracy / confusion: 99.91% / [[8537, 5, 0], [8, 7481, 0], [2, 3, 5052]], 
* Val   accuracy / confusion: 98.79% / [[1066, 3, 0], [11, 924, 3], [10, 5, 618]]

------------------------------ Epoch 146 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000022
- Iter 262 / 659, Loss: 0.000010
- Iter 393 / 659, Loss: 0.000077
- Iter 524 / 659, Loss: 0.000046
- Iter 655 / 659, Loss: 0.000013

* Train accuracy / confusion: 99.96% / [[8536, 3, 1], [2, 7491, 1], [0, 1, 5053]], 
* Val   accuracy / confusion: 98.45% / [[1062, 6, 1], [13, 920, 5], [9, 7, 617]]

------------------------------ Epoch 147 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.003935
- Iter 262 / 659, Loss: 0.001005
- Iter 393 / 659, Loss: 0.000191
- Iter 524 / 659, Loss: 0.000030
- Iter 655 / 659, Loss: 0.000217

* Train accuracy / confusion: 99.96% / [[8537, 1, 1], [3, 7491, 1], [1, 1, 5052]], 
* Val   accuracy / confusion: 98.64% / [[1062, 6, 1], [15, 918, 5], [3, 6, 624]]

------------------------------ Epoch 148 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000019
- Iter 262 / 659, Loss: 0.000126
- Iter 393 / 659, Loss: 0.000077
- Iter 524 / 659, Loss: 0.000115
- Iter 655 / 659, Loss: 0.000020

* Train accuracy / confusion: 99.97% / [[8540, 2, 3], [1, 7487, 1], [0, 0, 5054]], 
* Val   accuracy / confusion: 98.75% / [[1063, 4, 2], [13, 924, 1], [8, 5, 620]]

------------------------------ Epoch 149 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000363
- Iter 262 / 659, Loss: 0.000237
- Iter 393 / 659, Loss: 0.000052
- Iter 524 / 659, Loss: 0.000009
- Iter 655 / 659, Loss: 0.003885

* Train accuracy / confusion: 99.93% / [[8536, 6, 2], [4, 7487, 1], [1, 1, 5050]], 
* Val   accuracy / confusion: 98.75% / [[1064, 2, 3], [19, 915, 4], [2, 3, 628]]

------------------------------ Epoch 150 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000563
- Iter 262 / 659, Loss: 0.000027
- Iter 393 / 659, Loss: 0.000037
- Iter 524 / 659, Loss: 0.000367
- Iter 655 / 659, Loss: 0.000054

* Train accuracy / confusion: 99.96% / [[8538, 2, 2], [1, 7490, 0], [2, 2, 5051]], 
* Val   accuracy / confusion: 98.41% / [[1059, 5, 5], [10, 926, 2], [11, 9, 613]]

------------------------------ Epoch 151 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000013
- Iter 262 / 659, Loss: 0.000011
- Iter 393 / 659, Loss: 0.000119
- Iter 524 / 659, Loss: 0.000049
- Iter 655 / 659, Loss: 0.000002

* Train accuracy / confusion: 99.93% / [[8534, 4, 2], [7, 7484, 1], [1, 0, 5055]], 
* Val   accuracy / confusion: 98.83% / [[1064, 4, 1], [12, 924, 2], [5, 7, 621]]

------------------------------ Epoch 152 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000056
- Iter 262 / 659, Loss: 0.001374
- Iter 393 / 659, Loss: 0.000086
- Iter 524 / 659, Loss: 0.000378
- Iter 655 / 659, Loss: 0.000050

* Train accuracy / confusion: 99.96% / [[8536, 4, 2], [1, 7489, 0], [2, 0, 5054]], 
* Val   accuracy / confusion: 99.13% / [[1064, 4, 1], [9, 928, 1], [5, 3, 625]]

------------------------------ Epoch 153 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000045
- Iter 262 / 659, Loss: 0.000263
- Iter 393 / 659, Loss: 0.000075
- Iter 524 / 659, Loss: 0.000021
- Iter 655 / 659, Loss: 0.000063

* Train accuracy / confusion: 99.94% / [[8540, 5, 1], [4, 7481, 1], [0, 1, 5055]], 
* Val   accuracy / confusion: 98.83% / [[1066, 3, 0], [13, 923, 2], [7, 6, 620]]

------------------------------ Epoch 154 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.001545
- Iter 262 / 659, Loss: 0.001358
- Iter 393 / 659, Loss: 0.024019
- Iter 524 / 659, Loss: 0.000087
- Iter 655 / 659, Loss: 0.000122

* Train accuracy / confusion: 99.96% / [[8540, 2, 0], [5, 7484, 1], [0, 1, 5055]], 
* Val   accuracy / confusion: 98.79% / [[1063, 4, 2], [18, 918, 2], [3, 3, 627]]

------------------------------ Epoch 155 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000554
- Iter 262 / 659, Loss: 0.000090
- Iter 393 / 659, Loss: 0.000480
- Iter 524 / 659, Loss: 0.000005
- Iter 655 / 659, Loss: 0.000001

* Train accuracy / confusion: 99.97% / [[8546, 3, 0], [3, 7485, 0], [0, 0, 5051]], 
* Val   accuracy / confusion: 99.02% / [[1066, 2, 1], [13, 923, 2], [2, 6, 625]]

------------------------------ Epoch 156 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000312
- Iter 262 / 659, Loss: 0.000010
- Iter 393 / 659, Loss: 0.000032
- Iter 524 / 659, Loss: 0.000035
- Iter 655 / 659, Loss: 0.000179

* Train accuracy / confusion: 99.95% / [[8537, 3, 3], [4, 7487, 0], [0, 1, 5053]], 
* Val   accuracy / confusion: 98.90% / [[1065, 2, 2], [11, 923, 4], [6, 4, 623]]

------------------------------ Epoch 157 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000011
- Iter 262 / 659, Loss: 0.000411
- Iter 393 / 659, Loss: 0.000002
- Iter 524 / 659, Loss: 0.000028
- Iter 655 / 659, Loss: 0.000021

* Train accuracy / confusion: 99.96% / [[8545, 2, 0], [3, 7486, 1], [2, 1, 5048]], 
* Val   accuracy / confusion: 98.79% / [[1065, 4, 0], [15, 922, 1], [5, 7, 621]]

------------------------------ Epoch 158 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000054
- Iter 262 / 659, Loss: 0.000029
- Iter 393 / 659, Loss: 0.000181
- Iter 524 / 659, Loss: 0.000014
- Iter 655 / 659, Loss: 0.000017

* Train accuracy / confusion: 99.96% / [[8540, 3, 1], [2, 7489, 0], [2, 1, 5050]], 
* Val   accuracy / confusion: 98.86% / [[1065, 4, 0], [10, 924, 4], [5, 7, 621]]

------------------------------ Epoch 159 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000045
- Iter 262 / 659, Loss: 0.000054
- Iter 393 / 659, Loss: 0.003194
- Iter 524 / 659, Loss: 0.000004
- Iter 655 / 659, Loss: 0.000049

* Train accuracy / confusion: 99.97% / [[8546, 1, 2], [3, 7481, 0], [0, 1, 5054]], 
* Val   accuracy / confusion: 98.56% / [[1063, 4, 2], [16, 921, 1], [7, 8, 618]]

------------------------------ Epoch 160 / 200, Learning rate: 6.51e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000014
- Iter 262 / 659, Loss: 0.000036
- Iter 393 / 659, Loss: 0.000029
- Iter 524 / 659, Loss: 0.000051
- Iter 655 / 659, Loss: 0.000024

* Train accuracy / confusion: 99.98% / [[8541, 1, 2], [1, 7486, 0], [1, 0, 5056]], 
* Val   accuracy / confusion: 98.67% / [[1064, 4, 1], [17, 920, 1], [8, 4, 621]]

------------------------------ Epoch 161 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000001
- Iter 262 / 659, Loss: 0.000031
- Iter 393 / 659, Loss: 0.001202
- Iter 524 / 659, Loss: 0.000113
- Iter 655 / 659, Loss: 0.000079

* Train accuracy / confusion: 99.97% / [[8544, 1, 0], [3, 7483, 0], [2, 0, 5055]], 
* Val   accuracy / confusion: 98.64% / [[1064, 3, 2], [12, 920, 6], [8, 5, 620]]

------------------------------ Epoch 162 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000083
- Iter 262 / 659, Loss: 0.000073
- Iter 393 / 659, Loss: 0.000086
- Iter 524 / 659, Loss: 0.002432
- Iter 655 / 659, Loss: 0.000018

* Train accuracy / confusion: 99.94% / [[8539, 5, 0], [6, 7482, 0], [1, 0, 5055]], 
* Val   accuracy / confusion: 98.71% / [[1066, 3, 0], [18, 916, 4], [8, 1, 624]]

------------------------------ Epoch 163 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000076
- Iter 262 / 659, Loss: 0.001363
- Iter 393 / 659, Loss: 0.000043
- Iter 524 / 659, Loss: 0.000084
- Iter 655 / 659, Loss: 0.000066

* Train accuracy / confusion: 99.96% / [[8541, 2, 0], [2, 7487, 2], [1, 2, 5051]], 
* Val   accuracy / confusion: 98.83% / [[1065, 3, 1], [12, 922, 4], [5, 6, 622]]

------------------------------ Epoch 164 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000007
- Iter 262 / 659, Loss: 0.028077
- Iter 393 / 659, Loss: 0.000001
- Iter 524 / 659, Loss: 0.000002
- Iter 655 / 659, Loss: 0.001477

* Train accuracy / confusion: 99.97% / [[8541, 0, 0], [5, 7487, 0], [0, 1, 5054]], 
* Val   accuracy / confusion: 98.79% / [[1065, 2, 2], [15, 921, 2], [8, 3, 622]]

------------------------------ Epoch 165 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000004
- Iter 262 / 659, Loss: 0.000031
- Iter 393 / 659, Loss: 0.000014
- Iter 524 / 659, Loss: 0.000011
- Iter 655 / 659, Loss: 0.000073

* Train accuracy / confusion: 99.98% / [[8543, 0, 2], [0, 7489, 0], [2, 0, 5052]], 
* Val   accuracy / confusion: 98.48% / [[1059, 6, 4], [14, 921, 3], [8, 5, 620]]

------------------------------ Epoch 166 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000012
- Iter 262 / 659, Loss: 0.000059
- Iter 393 / 659, Loss: 0.000907
- Iter 524 / 659, Loss: 0.000015
- Iter 655 / 659, Loss: 0.000030

* Train accuracy / confusion: 99.95% / [[8539, 3, 1], [4, 7488, 0], [2, 0, 5051]], 
* Val   accuracy / confusion: 98.79% / [[1065, 3, 1], [12, 922, 4], [8, 4, 621]]

------------------------------ Epoch 167 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000002
- Iter 262 / 659, Loss: 0.000023
- Iter 393 / 659, Loss: 0.000013
- Iter 524 / 659, Loss: 0.000243
- Iter 655 / 659, Loss: 0.000064

* Train accuracy / confusion: 99.97% / [[8537, 2, 0], [2, 7488, 0], [1, 1, 5057]], 
* Val   accuracy / confusion: 98.79% / [[1062, 6, 1], [13, 923, 2], [9, 1, 623]]

------------------------------ Epoch 168 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000029
- Iter 262 / 659, Loss: 0.000651
- Iter 393 / 659, Loss: 0.000057
- Iter 524 / 659, Loss: 0.000001
- Iter 655 / 659, Loss: 0.000026

* Train accuracy / confusion: 99.98% / [[8540, 1, 0], [3, 7491, 0], [0, 0, 5053]], 
* Val   accuracy / confusion: 98.83% / [[1067, 2, 0], [12, 923, 3], [11, 3, 619]]

------------------------------ Epoch 169 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000091
- Iter 262 / 659, Loss: 0.000376
- Iter 393 / 659, Loss: 0.000037
- Iter 524 / 659, Loss: 0.000054
- Iter 655 / 659, Loss: 0.000005

* Train accuracy / confusion: 99.96% / [[8540, 1, 0], [2, 7487, 1], [1, 3, 5053]], 
* Val   accuracy / confusion: 98.52% / [[1061, 4, 4], [15, 918, 5], [7, 4, 622]]

------------------------------ Epoch 170 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.006932
- Iter 262 / 659, Loss: 0.000028
- Iter 393 / 659, Loss: 0.000003
- Iter 524 / 659, Loss: 0.002067
- Iter 655 / 659, Loss: 0.000016

* Train accuracy / confusion: 100.00% / [[8539, 0, 0], [0, 7491, 1], [0, 0, 5057]], 
* Val   accuracy / confusion: 98.71% / [[1063, 5, 1], [16, 919, 3], [6, 3, 624]]

------------------------------ Epoch 171 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.001033
- Iter 262 / 659, Loss: 0.000175
- Iter 393 / 659, Loss: 0.000002
- Iter 524 / 659, Loss: 0.002648
- Iter 655 / 659, Loss: 0.000123

* Train accuracy / confusion: 99.97% / [[8543, 1, 2], [2, 7488, 0], [1, 0, 5051]], 
* Val   accuracy / confusion: 98.71% / [[1063, 6, 0], [15, 920, 3], [5, 5, 623]]

------------------------------ Epoch 172 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000094
- Iter 262 / 659, Loss: 0.000012
- Iter 393 / 659, Loss: 0.000786
- Iter 524 / 659, Loss: 0.000048
- Iter 655 / 659, Loss: 0.000068

* Train accuracy / confusion: 99.98% / [[8542, 0, 0], [2, 7490, 0], [1, 1, 5052]], 
* Val   accuracy / confusion: 98.67% / [[1060, 6, 3], [13, 922, 3], [7, 3, 623]]

------------------------------ Epoch 173 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000003
- Iter 262 / 659, Loss: 0.000005
- Iter 393 / 659, Loss: 0.000002
- Iter 524 / 659, Loss: 0.000166
- Iter 655 / 659, Loss: 0.000144

* Train accuracy / confusion: 99.95% / [[8536, 4, 1], [4, 7489, 0], [1, 0, 5053]], 
* Val   accuracy / confusion: 98.98% / [[1063, 5, 1], [9, 926, 3], [5, 4, 624]]

------------------------------ Epoch 174 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000004
- Iter 262 / 659, Loss: 0.026536
- Iter 393 / 659, Loss: 0.000003
- Iter 524 / 659, Loss: 0.000000
- Iter 655 / 659, Loss: 0.000762

* Train accuracy / confusion: 99.99% / [[8540, 1, 0], [1, 7493, 0], [0, 0, 5053]], 
* Val   accuracy / confusion: 98.71% / [[1065, 2, 2], [13, 920, 5], [9, 3, 621]]

------------------------------ Epoch 175 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000006
- Iter 262 / 659, Loss: 0.000032
- Iter 393 / 659, Loss: 0.000022
- Iter 524 / 659, Loss: 0.000145
- Iter 655 / 659, Loss: 0.000003

* Train accuracy / confusion: 99.98% / [[8540, 3, 2], [0, 7487, 0], [0, 0, 5056]], 
* Val   accuracy / confusion: 98.94% / [[1066, 2, 1], [13, 923, 2], [6, 4, 623]]

------------------------------ Epoch 176 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000040
- Iter 262 / 659, Loss: 0.000030
- Iter 393 / 659, Loss: 0.000022
- Iter 524 / 659, Loss: 0.000005
- Iter 655 / 659, Loss: 0.022925

* Train accuracy / confusion: 99.97% / [[8540, 3, 1], [2, 7491, 0], [0, 0, 5051]], 
* Val   accuracy / confusion: 98.83% / [[1066, 3, 0], [14, 921, 3], [5, 6, 622]]

------------------------------ Epoch 177 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000016
- Iter 262 / 659, Loss: 0.000027
- Iter 393 / 659, Loss: 0.000004
- Iter 524 / 659, Loss: 0.000227
- Iter 655 / 659, Loss: 0.000552

* Train accuracy / confusion: 99.98% / [[8540, 2, 1], [0, 7489, 0], [1, 1, 5054]], 
* Val   accuracy / confusion: 98.71% / [[1063, 5, 1], [15, 919, 4], [5, 4, 624]]

------------------------------ Epoch 178 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000001
- Iter 262 / 659, Loss: 0.001728
- Iter 393 / 659, Loss: 0.000018
- Iter 524 / 659, Loss: 0.000011
- Iter 655 / 659, Loss: 0.000012

* Train accuracy / confusion: 99.99% / [[8541, 0, 0], [0, 7491, 2], [1, 0, 5053]], 
* Val   accuracy / confusion: 98.83% / [[1062, 7, 0], [11, 926, 1], [6, 6, 621]]

------------------------------ Epoch 179 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000190
- Iter 262 / 659, Loss: 0.000043
- Iter 393 / 659, Loss: 0.000064
- Iter 524 / 659, Loss: 0.000204
- Iter 655 / 659, Loss: 0.000131

* Train accuracy / confusion: 99.98% / [[8542, 2, 1], [1, 7486, 0], [0, 0, 5056]], 
* Val   accuracy / confusion: 98.94% / [[1064, 4, 1], [14, 920, 4], [4, 1, 628]]

------------------------------ Epoch 180 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000043
- Iter 262 / 659, Loss: 0.000014
- Iter 393 / 659, Loss: 0.002153
- Iter 524 / 659, Loss: 0.000015
- Iter 655 / 659, Loss: 0.000060

* Train accuracy / confusion: 99.96% / [[8545, 2, 1], [2, 7482, 2], [1, 0, 5053]], 
* Val   accuracy / confusion: 98.60% / [[1063, 4, 2], [12, 923, 3], [9, 7, 617]]

------------------------------ Epoch 181 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000141
- Iter 262 / 659, Loss: 0.000044
- Iter 393 / 659, Loss: 0.000277
- Iter 524 / 659, Loss: 0.000121
- Iter 655 / 659, Loss: 0.000064

* Train accuracy / confusion: 99.98% / [[8541, 1, 0], [2, 7489, 2], [0, 0, 5053]], 
* Val   accuracy / confusion: 98.79% / [[1065, 3, 1], [16, 922, 0], [7, 5, 621]]

------------------------------ Epoch 182 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000084
- Iter 262 / 659, Loss: 0.000006
- Iter 393 / 659, Loss: 0.000019
- Iter 524 / 659, Loss: 0.000234
- Iter 655 / 659, Loss: 0.000023

* Train accuracy / confusion: 99.98% / [[8536, 3, 1], [0, 7493, 0], [1, 0, 5054]], 
* Val   accuracy / confusion: 98.45% / [[1064, 5, 0], [17, 917, 4], [8, 7, 618]]

------------------------------ Epoch 183 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000003
- Iter 262 / 659, Loss: 0.000124
- Iter 393 / 659, Loss: 0.000441
- Iter 524 / 659, Loss: 0.000041
- Iter 655 / 659, Loss: 0.000029

* Train accuracy / confusion: 99.98% / [[8544, 0, 0], [1, 7487, 0], [3, 0, 5053]], 
* Val   accuracy / confusion: 98.75% / [[1064, 3, 2], [20, 918, 0], [7, 1, 625]]

------------------------------ Epoch 184 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000296
- Iter 262 / 659, Loss: 0.000026
- Iter 393 / 659, Loss: 0.000003
- Iter 524 / 659, Loss: 0.000011
- Iter 655 / 659, Loss: 0.002067

* Train accuracy / confusion: 99.97% / [[8536, 1, 1], [4, 7487, 0], [0, 1, 5058]], 
* Val   accuracy / confusion: 99.02% / [[1061, 6, 2], [9, 927, 2], [3, 4, 626]]

------------------------------ Epoch 185 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000182
- Iter 262 / 659, Loss: 0.000036
- Iter 393 / 659, Loss: 0.000020
- Iter 524 / 659, Loss: 0.000023
- Iter 655 / 659, Loss: 0.000088

* Train accuracy / confusion: 99.97% / [[8539, 0, 2], [2, 7485, 1], [1, 1, 5057]], 
* Val   accuracy / confusion: 98.52% / [[1060, 4, 5], [17, 916, 5], [4, 4, 625]]

------------------------------ Epoch 186 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000002
- Iter 262 / 659, Loss: 0.007402
- Iter 393 / 659, Loss: 0.000001
- Iter 524 / 659, Loss: 0.003553
- Iter 655 / 659, Loss: 0.000017

* Train accuracy / confusion: 99.99% / [[8539, 1, 0], [1, 7490, 0], [0, 1, 5056]], 
* Val   accuracy / confusion: 98.79% / [[1063, 4, 2], [8, 928, 2], [8, 8, 617]]

------------------------------ Epoch 187 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000138
- Iter 262 / 659, Loss: 0.002342
- Iter 393 / 659, Loss: 0.000020
- Iter 524 / 659, Loss: 0.000062
- Iter 655 / 659, Loss: 0.000203

* Train accuracy / confusion: 99.99% / [[8542, 1, 1], [1, 7487, 0], [0, 0, 5056]], 
* Val   accuracy / confusion: 98.94% / [[1065, 3, 1], [12, 922, 4], [5, 3, 625]]

------------------------------ Epoch 188 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000329
- Iter 262 / 659, Loss: 0.000001
- Iter 393 / 659, Loss: 0.000024
- Iter 524 / 659, Loss: 0.000003
- Iter 655 / 659, Loss: 0.000060

* Train accuracy / confusion: 99.98% / [[8543, 1, 0], [3, 7485, 0], [1, 0, 5055]], 
* Val   accuracy / confusion: 98.56% / [[1063, 4, 2], [17, 919, 2], [7, 6, 620]]

------------------------------ Epoch 189 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000009
- Iter 262 / 659, Loss: 0.000013
- Iter 393 / 659, Loss: 0.000014
- Iter 524 / 659, Loss: 0.000015
- Iter 655 / 659, Loss: 0.000868

* Train accuracy / confusion: 99.99% / [[8545, 0, 0], [0, 7492, 0], [0, 3, 5048]], 
* Val   accuracy / confusion: 98.67% / [[1067, 2, 0], [17, 918, 3], [9, 4, 620]]

------------------------------ Epoch 190 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000224
- Iter 262 / 659, Loss: 0.000015
- Iter 393 / 659, Loss: 0.000091
- Iter 524 / 659, Loss: 0.000005
- Iter 655 / 659, Loss: 0.000005

* Train accuracy / confusion: 99.99% / [[8541, 0, 0], [2, 7490, 1], [0, 0, 5054]], 
* Val   accuracy / confusion: 98.71% / [[1063, 5, 1], [14, 923, 1], [5, 8, 620]]

------------------------------ Epoch 191 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000099
- Iter 262 / 659, Loss: 0.000030
- Iter 393 / 659, Loss: 0.000010
- Iter 524 / 659, Loss: 0.000095
- Iter 655 / 659, Loss: 0.000005

* Train accuracy / confusion: 99.98% / [[8537, 3, 0], [1, 7491, 0], [0, 1, 5055]], 
* Val   accuracy / confusion: 98.83% / [[1062, 7, 0], [12, 923, 3], [4, 5, 624]]

------------------------------ Epoch 192 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000059
- Iter 262 / 659, Loss: 0.000003
- Iter 393 / 659, Loss: 0.000003
- Iter 524 / 659, Loss: 0.000038
- Iter 655 / 659, Loss: 0.000016

* Train accuracy / confusion: 99.98% / [[8539, 3, 0], [0, 7490, 2], [0, 0, 5054]], 
* Val   accuracy / confusion: 98.75% / [[1060, 7, 2], [11, 925, 2], [6, 5, 622]]

------------------------------ Epoch 193 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000002
- Iter 262 / 659, Loss: 0.000005
- Iter 393 / 659, Loss: 0.000009
- Iter 524 / 659, Loss: 0.000002
- Iter 655 / 659, Loss: 0.000033

* Train accuracy / confusion: 99.99% / [[8540, 1, 1], [1, 7490, 0], [0, 0, 5055]], 
* Val   accuracy / confusion: 98.48% / [[1064, 4, 1], [16, 918, 4], [9, 6, 618]]

------------------------------ Epoch 194 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000536
- Iter 262 / 659, Loss: 0.000007
- Iter 393 / 659, Loss: 0.000003
- Iter 524 / 659, Loss: 0.001846
- Iter 655 / 659, Loss: 0.000181

* Train accuracy / confusion: 100.00% / [[8539, 0, 0], [0, 7495, 0], [0, 0, 5054]], 
* Val   accuracy / confusion: 98.67% / [[1062, 5, 2], [16, 921, 1], [5, 6, 622]]

------------------------------ Epoch 195 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000144
- Iter 262 / 659, Loss: 0.008667
- Iter 393 / 659, Loss: 0.000270
- Iter 524 / 659, Loss: 0.000003
- Iter 655 / 659, Loss: 0.001145

* Train accuracy / confusion: 99.99% / [[8539, 2, 0], [0, 7491, 1], [0, 0, 5055]], 
* Val   accuracy / confusion: 98.71% / [[1063, 3, 3], [14, 921, 3], [5, 6, 622]]

------------------------------ Epoch 196 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000382
- Iter 262 / 659, Loss: 0.000004
- Iter 393 / 659, Loss: 0.000007
- Iter 524 / 659, Loss: 0.000041
- Iter 655 / 659, Loss: 0.000004

* Train accuracy / confusion: 99.98% / [[8541, 1, 1], [3, 7485, 0], [0, 0, 5057]], 
* Val   accuracy / confusion: 98.60% / [[1062, 5, 2], [14, 921, 3], [5, 8, 620]]

------------------------------ Epoch 197 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000035
- Iter 262 / 659, Loss: 0.000022
- Iter 393 / 659, Loss: 0.000014
- Iter 524 / 659, Loss: 0.000331
- Iter 655 / 659, Loss: 0.000002

* Train accuracy / confusion: 99.97% / [[8542, 3, 0], [1, 7485, 1], [1, 1, 5054]], 
* Val   accuracy / confusion: 99.05% / [[1066, 2, 1], [9, 926, 3], [9, 1, 623]]

------------------------------ Epoch 198 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000624
- Iter 262 / 659, Loss: 0.000012
- Iter 393 / 659, Loss: 0.000338
- Iter 524 / 659, Loss: 0.000057
- Iter 655 / 659, Loss: 0.000364

* Train accuracy / confusion: 99.97% / [[8538, 3, 0], [2, 7488, 0], [1, 1, 5055]], 
* Val   accuracy / confusion: 98.45% / [[1060, 8, 1], [15, 915, 8], [4, 5, 624]]

------------------------------ Epoch 199 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000442
- Iter 262 / 659, Loss: 0.000028
- Iter 393 / 659, Loss: 0.000400
- Iter 524 / 659, Loss: 0.000714
- Iter 655 / 659, Loss: 0.002455

* Train accuracy / confusion: 99.99% / [[8544, 1, 0], [1, 7487, 0], [0, 1, 5054]], 
* Val   accuracy / confusion: 99.09% / [[1065, 3, 1], [7, 930, 1], [5, 7, 621]]

------------------------------ Epoch 200 / 200, Learning rate: 6.51e-06 ------------------------------
- Iter 131 / 659, Loss: 0.000050
- Iter 262 / 659, Loss: 0.002913
- Iter 393 / 659, Loss: 0.000007
- Iter 524 / 659, Loss: 0.000028
- Iter 655 / 659, Loss: 0.000056

* Train accuracy / confusion: 99.97% / [[8542, 1, 0], [5, 7488, 0], [0, 1, 5051]], 
* Val   accuracy / confusion: 98.79% / [[1063, 4, 2], [16, 921, 1], [6, 3, 624]]

**************************************** Training Ends ****************************************
- Test accuracy: 98.81%

- Confusion matrix:
 [[31929   105    36]
 [  471 27470   169]
 [   65    98 18797]]

print('- Debug table:')
pprint.pp(test_debug, indent=2, width=100)

- Debug table:
{ '14546': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00865039_170816'},
  '00719': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00164098_041214'},
  '30664': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01303196_020317'},
  '04086': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00498791_260916'},
  '05765': {'GT': 2, 'Acc': ' 76.67%', 'Pred': [0, 7, 23], 'edfname': '00623681_070415'},
  '19208': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01008809_021017'},
  '19516': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01019016_241115'},
  '36174': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01430323_060819'},
  '12726': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00796686_020819'},
  '31736': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01324136_201217'},
  '07979': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00673505_020419'},
  '18204': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00971724_030414'},
  '10785': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00740750_110315'},
  '17786': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00958455_200917'},
  '06324': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00634467_180717'},
  '12220': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00776486_121016'},
  '13629': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00824216_080416'},
  '04527': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00601765_231118'},
  '23994': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01154834_180417'},
  '03080': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00406530_310719'},
  '30733': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01303263_281116'},
  '23424': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01147097_121119'},
  '24505': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01164481_220615'},
  '18902': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00988618_270618'},
  '32089': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01334787_211117'},
  '26285': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01212635_270515'},
  '16435': {'GT': 2, 'Acc': ' 80.00%', 'Pred': [6, 0, 24], 'edfname': '00926040_061115'},
  '19711': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01026841_270116'},
  '02693': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00388436_200616'},
  '06604': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00642740_120918'},
  '28610': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01266829_070416'},
  '17149': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00951066_131217'},
  '25105': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01186214'},
  '03473': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00430821_100217'},
  '30539': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01300391_251116'},
  '27284': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01240949_191015'},
  '31301': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01312293_120417'},
  '25411': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01195277_151119'},
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  '27877': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01251650_090819'},
  '33562': {'GT': 1, 'Acc': ' 20.00%', 'Pred': [0, 6, 24], 'edfname': '01361792_301018'},
  '08587': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00685191_100419'},
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  '14995': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00883719_240812'},
  '31058': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01307080_060117'},
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  '04913': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00607068_260617'},
  '27462': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01242661_011215'},
  '03867': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00480292_231215'},
  '22931': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01139949_060214'},
  '12863': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00799528_270916'},
  '14529': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00862802_260618'},
  '07643': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00666711_140218'},
  '17846': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00960161_201014'},
  '27400': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01241989_101115'},
  '32363': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01338642_081119'},
  '17859': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00961326_010817'},
  '31027': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01306737_151216'},
  '04543': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00601765_231118'},
  '06608': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00642740_120918'},
  '21611': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01127053_290818'},
  '06922': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00649687_041019'},
  '16470': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00926040_121219'},
  '30372': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [0, 25, 5], 'edfname': '01298381_281016'},
  '04140': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00502785_041019'},
  '26936': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01235034_120220'},
  '30461': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01299709_090419'},
  '02329': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00343454_020414'},
  '06181': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00632465_150414'},
  '12827': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00796775_301115'},
  '26891': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01235034_021117'},
  '30484': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01299709_090419'},
  '22137': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01134557_301116'},
  '14339': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00856756_131119'},
  '31377': {'GT': 1, 'Acc': ' 33.33%', 'Pred': [20, 10, 0], 'edfname': '01314786_200317'},
  '03609': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00450712_161118'},
  '13119': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00805584_260719'},
  '24464': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01163965_130718'},
  '05246': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00614191_150617'},
  '03840': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00480292_181215'},
  '17156': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00951066_131217'},
  '17166': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00951066_131217'},
  '14616': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00867679_291117'},
  '03482': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00430821_100217'},
  '34085': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01372947_130318'},
  '34459': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01382697_261218'},
  '10544': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00735258_051218'},
  '26470': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01216470_141118'},
  '25587': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01200248_290120'},
  '00673': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00164098_021019'},
  '31060': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01307080_060117'},
  '31636': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01321744_130417'},
  '14281': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00852650_170818'},
  '20388': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01057134_310316'},
  '00828': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00164098_250416'},
  '02923': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00399896_180414'},
  '05566': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00619387_070317'},
  '14637': {'GT': 1, 'Acc': ' 56.67%', 'Pred': [13, 17, 0], 'edfname': '00869351_160217'},
  '33643': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01364159_110118'},
  '12764': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00796686_090819'},
  '18981': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [1, 0, 29], 'edfname': '01000945_250615'},
  '28126': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01254424_120118'},
  '26117': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01209182_270415'},
  '19530': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 0, 4], 'edfname': '01020014_151215'},
  '08815': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00690996_100817'},
  '13360': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00816768_260618'},
  '03633': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '00456090_140417'},
  '28110': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01254379_210116'},
  '11219': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00752398_110214'},
  '05864': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00626957_010617'},
  '24130': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01155763_120514'},
  '35776': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01418070_200819'},
  '01800': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00293228_120718'},
  '28558': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01266696_300316'},
  '03749': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00474121_161118'},
  '29140': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01277867_160616'},
  '07483': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00663561_030414'},
  '14428': {'GT': 2, 'Acc': ' 13.33%', 'Pred': [8, 18, 4], 'edfname': '00860983_271114'},
  '23718': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01149087_170414'},
  '24489': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01163965_130718'},
  '14686': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00871822_070918'},
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  '13954': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00840844_071118'},
  '25372': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01195277_011119'},
  '25783': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01201484_150618'},
  '19358': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01011922_270815'},
  '08603': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00685248_060515'},
  '36441': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01464624_011119'},
  '11358': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00753842_110517'},
  '01934': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00313383_180314'},
  '18762': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00987555_270717'},
  '17167': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00951066_131217'},
  '11164': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00752045_260419'},
  '03406': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00427010_301118'},
  '03035': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00406530_120419'},
  '27649': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01243336_240117'},
  '19702': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01026841_270116'},
  '10109': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00723144_180118'},
  '30352': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01298112_271016'},
  '06355': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00635370_170220'},
  '33011': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01350206_021117'},
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  '13990': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00840844_250119'},
  '27559': {'GT': 1, 'Acc': ' 66.67%', 'Pred': [10, 20, 0], 'edfname': '01242982_110416'},
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  '03761': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00474121_161118'},
  '05437': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00617308_021216'},
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  '05885': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00626957_040417'},
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  '36203': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01432133_160519'},
  '18642': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00986061_130716'},
  '11762': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00763580_281118'},
  '25710': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01201052_060217'},
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  '32936': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01348158_231019'},
  '20343': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01056827_240818'},
  '21954': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01133652_250216'},
  '00676': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00164098_021019'},
  '23271': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01145402_161117'},
  '00769': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00164098_180919'},
  '34106': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01372947_240518'},
  '06090': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00631178_110517'},
  '28982': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01274934_190516'},
  '20751': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01074533_071116'},
  '14202': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00851302_261217'},
  '32912': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01348158_231019'},
  '18177': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00971612_150317'},
  '26066': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01209181_250315'},
  '07977': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00673505_020419'},
  '21591': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01126210_280414'},
  '18230': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00977111_211218'},
  '08692': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '00687089_011216'},
  '27959': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01252396_141216'},
  '21967': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01133652_250216'},
  '14474': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00861148_300119'},
  '18101': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00967966_110517'},
  '26750': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01225385_060815'},
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  '01312': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00244987_050315'},
  '03994': {'GT': 0, 'Acc': ' 70.00%', 'Pred': [21, 9, 0], 'edfname': '00487637_220715'},
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  '14737': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00873142_220319'},
  '16883': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00945146_011214'},
  '17306': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 0, 4], 'edfname': '00952475_210518'},
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  '08388': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00678916_020718'},
  '03988': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '00487637_220715'},
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  '12331': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [0, 26, 4], 'edfname': '00779511_050319'},
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  '27027': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01235272_051115'},
  '26340': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [3, 27, 0], 'edfname': '01215115_270715'},
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  '25591': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01200378_230117'},
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  '17212': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00951808_251116'},
  '16200': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00916265_090818'},
  '07375': {'GT': 1, 'Acc': ' 53.33%', 'Pred': [6, 16, 8], 'edfname': '00659706_220218'},
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  '22877': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01139946_120214'},
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  '08528': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00684846_030717'},
  '03621': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 0, 2], 'edfname': '00456090_140417'},
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  '12345': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00779511_050319'},
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  '00359': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00115426_080219'},
  '06983': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00649687_180919'},
  '10755': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00740694_110315'},
  '20539': {'GT': 2, 'Acc': ' 53.33%', 'Pred': [10, 4, 16], 'edfname': '01064740_261015'},
  '29728': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01288482_220816'},
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  '15761': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00899964_060115'},
  '15987': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00906868_231215'},
  '00675': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00164098_021019'},
  '29856': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01290676_090617'},
  '35049': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01402378_110219'},
  '30211': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01296557_010819'},
  '26570': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01220269_141218'},
  '19812': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01027623_060317'},
  '12464': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '00784417_100315'},
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  '23572': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01147102_080414'},
  '15008': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00885321_170314'},
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  '11142': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00747390_250718'},
  '26688': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 4, 0], 'edfname': '01225123_070715'},
  '35021': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01402179_311018'},
  '31391': {'GT': 1, 'Acc': ' 63.33%', 'Pred': [11, 19, 0], 'edfname': '01314786_200317'},
  '29832': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01289759_110517'},
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  '16473': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00926040_121219'},
  '01125': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00232310_261218'},
  '00028': {'GT': 0, 'Acc': ' 56.67%', 'Pred': [17, 13, 0], 'edfname': '00029426_020817'},
  '08295': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00676993_310315'},
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  '09459': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00708411_091118'},
  '13481': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00817092_020118'},
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  '34246': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01377415_200418'},
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  '25239': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01190283_261114'},
  '11798': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00764690_050319'},
  '22621': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01139924_060417'},
  '03089': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00406530_310719'},
  '00091': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00048377_070819'},
  '32046': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01334570_070717'},
  '10989': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00744618_290319'},
  '27319': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01241945_191115'},
  '20504': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01064650_161117'},
  '01926': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00313383_180314'},
  '25354': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '01193508_171214'},
  '07657': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00666711_140218'},
  '27262': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01240162_121015'},
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  '01483': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00265504_101016'},
  '03669': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00458897_141016'},
  '11652': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00761575_090418'},
  '27085': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01235273_051015'},
  '25913': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01203444_251017'},
  '01478': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00265504_101016'},
  '12110': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '00773108_250615'},
  '08126': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00675516_090120'},
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  '22392': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01137650_290819'},
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  '27901': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01251650_191219'},
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  '00514': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00138259_030518'},
  '10165': {'GT': 1, 'Acc': ' 76.67%', 'Pred': [0, 23, 7], 'edfname': '00725683_091115'},
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  '29908': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01293646_120719'},
  '13882': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00840267_160317'},
  '31278': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '01311956_130117'},
  '04994': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00608836_270219'},
  '20036': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01035162_180119'},
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  '34612': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01392274_020320'},
  '15723': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '00899695_280918'},
  '14234': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [5, 25, 0], 'edfname': '00851511_101018'},
  '06879': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00649687_010715'},
  '25021': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01182545_070316'},
  '06278': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00634366_241019'},
  '30252': {'GT': 1, 'Acc': ' 50.00%', 'Pred': [0, 15, 15], 'edfname': '01296557_160120'},
  '15581': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00896386_240914'},
  '08686': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00687089_011216'},
  '25030': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01182608_201014'},
  '34666': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01392607_260718'},
  '09618': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00713432_210519'},
  '20418': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01063007_171115'},
  '02019': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00316358_050815'},
  '14142': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00848071_121018'},
  '16838': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00941070_050617'},
  '25113': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01186214'},
  '05423': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00617283_230318'},
  '15420': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00891889_060717'},
  '25301': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01191562_091214'},
  '26001': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01207886_270515'},
  '30917': {'GT': 1, 'Acc': ' 73.33%', 'Pred': [0, 22, 8], 'edfname': '01306496_130117'},
  '16383': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00919596_200117'},
  '14816': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00875350_140514'},
  '05365': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00616193_090218'},
  '10113': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00723144_180118'},
  '11778': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00763580_281118'},
  '04576': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00602435_270217'},
  '19620': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01025734_250815'},
  '32313': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01338557_190717'},
  '00677': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00164098_021019'},
  '27923': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01252335_211016'},
  '07994': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00673505_020419'},
  '12420': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00783510_301115'},
  '05874': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00626957_040417'},
  '07153': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00654671_151018'},
  '06138': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00631526_070716'},
  '03528': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00430974_050315'},
  '35936': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01422773_010419'},
  '29825': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01289759_110517'},
  '35116': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01403371_201218'},
  '03465': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00430821_031116'},
  '25578': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01200248_290120'},
  '03422': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00429185_130917'},
  '06523': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '00641692_170315'},
  '09978': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00721815_280519'},
  '00661': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00162025_221215'},
  '15495': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00894185_250817'},
  '24372': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01159816_310315'},
  '24503': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01164481_220615'},
  '30275': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01297715_101116'},
  '26132': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01209182_270415'},
  '30324': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01297810_151116'},
  '13416': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00817022_010415'},
  '01243': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00241549_060417'},
  '00189': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00050941_130116'},
  '31647': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01321744_130417'},
  '24331': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01159816_060515'},
  '31401': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01314894_190517'},
  '15503': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00894185_250817'},
  '12813': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00796775_281015'},
  '03074': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00406530_140819'},
  '07967': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00673491_141014'},
  '00311': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00110793_210917'},
  '03230': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00414635_240414'},
  '00509': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00138259_030518'},
  '03734': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00472015_120314'},
  '29118': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 0, 1], 'edfname': '01277193_080616'},
  '01042': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00223407_161219'},
  '23354': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01146683_300419'},
  '07559': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00665084_280219'},
  '23373': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '01146683_300419'},
  '09099': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00698863_020419'},
  '14847': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00876310_261217'},
  '19480': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01018913_161018'},
  '25217': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01190283_261114'},
  '08171': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00675516_231019'},
  '15750': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00899964_060115'},
  '22623': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01139924_060417'},
  '05125': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00611999_041018'},
  '22075': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01134450_161019'},
  '13418': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00817022_010415'},
  '18422': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00980362_020616'},
  '09979': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00721815_280519'},
  '17223': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00951808_251116'},
  '31648': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01321744_130417'},
  '09513': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00709574_191217'},
  '07847': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00671379_290617'},
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  '11181': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00752045_260419'},
  '00929': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00209742_081217'},
  '36338': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01450766_200919'},
  '06268': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00634366_241019'},
  '04307': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00518633_031215'},
  '20355': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01057134_240216'},
  '12887': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00799528_270916'},
  '19570': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01020558_160316'},
  '02834': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00396894_030119'},
  '04574': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00602435_270217'},
  '14926': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00879020_080817'},
  '14197': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00851302_261217'},
  '02999': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00401196_061218'},
  '23147': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01141791_270214'},
  '13061': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00805584_060717'},
  '31869': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01329562_150518'},
  '23936': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '01154159_230414'},
  '07944': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00673491_141014'},
  '01827': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00295596_101016'},
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  '10075': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00723110_240419'},
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  '34631': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01392274_020320'},
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  '05715': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00621729_020616'},
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  '03748': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00474121_161118'},
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  '36312': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01446092_160819'},
  '32700': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01344212_240817'},
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  '28846': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01272370_271217'},
  '24387': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01159816_310315'},
  '08575': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00685191_100419'},
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  '03762': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00474121_161118'},
  '12762': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00796686_090819'},
  '12336': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00779511_050319'},
  '02481': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00354396_021116'},
  '19531': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01020014_151215'},
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  '05156': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00612060_221118'},
  '00021': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00001809_261018'},
  '32677': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01344212_220819'},
  '29259': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01279168_171219'},
  '04306': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00518633_031215'},
  '29277': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01279168_171219'},
  '28948': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01274934'},
  '15114': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00885992_011216'},
  '31387': {'GT': 1, 'Acc': ' 56.67%', 'Pred': [13, 17, 0], 'edfname': '01314786_200317'},
  '36394': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01457795_141019'},
  '18331': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00978276_281114'},
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  '04049': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00489389_250319'},
  '05631': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00620551_141215'},
  '15083': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00885959_160419'},
  '13970': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00840844_250119'},
  '16484': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00926040_201119'},
  '18356': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00978569_110119'},
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  '03549': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00433859_230519'},
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  '04165': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00502785_121219'},
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  '16443': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [2, 0, 28], 'edfname': '00926040_061115'},
  '22103': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01134450_300919'},
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  '13542': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00817768_291118'},
  '34239': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01377415_200418'},
  '34236': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01376302_290318'},
  '28814': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01271298_270319'},
  '30385': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01298381_281016'},
  '10833': {'GT': 1, 'Acc': ' 13.33%', 'Pred': [26, 4, 0], 'edfname': '00741780_281116'},
  '06517': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00641692_170315'},
  '20462': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01063145_220915'},
  '30594': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01301982_151116'},
  '22646': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01139924_060417'},
  '23087': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01141787_190515'},
  '12915': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00803113_260516'},
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  '11623': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00760780_141118'},
  '29390': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01280762_171116'},
  '14452': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00861148_300119'},
  '15283': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00888613_080618'},
  '05833': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00626173_030118'},
  '27683': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01246499_301115'},
  '20294': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01054491_080116'},
  '02020': {'GT': 0, 'Acc': ' 76.67%', 'Pred': [23, 7, 0], 'edfname': '00316358_050815'},
  '16265': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00916613_240813'},
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  '16165': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00915077_310818'},
  '31028': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01306737_151216'},
  '27369': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01241947_261115'},
  '23951': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01154528_150514'},
  '07677': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00666711_150316'},
  '01180': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00233423_111218'},
  '25840': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01203444_090819'},
  '20720': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01073369_130514'},
  '09228': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00699668_310818'},
  '18961': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00996941_180517'},
  '14758': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '00875320_070415'},
  '32944': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01348263_210917'},
  '03069': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00406530_140819'},
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  '08160': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00675516_190919'},
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  '11215': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00752398_110214'},
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  '19712': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01026990_241215'},
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  '04817': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00606315_300715'},
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  '15921': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00906868_071216'},
  '16925': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00945348_170215'},
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  '07941': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00672867_031116'},
  '24073': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01155730_070514'},
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  '23974': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01154834_011019'},
  '22873': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01139945_040314'},
  '24209': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01156406_120319'},
  '31738': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01324136_201217'},
  '33934': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01368637_230218'},
  '13875': {'GT': 2, 'Acc': ' 76.67%', 'Pred': [0, 7, 23], 'edfname': '00840267_160317'},
  '14067': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00847087_220219'},
  '32438': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01338710_261017'},
  '22372': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01137650_290819'},
  '12209': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00776486_121016'},
  '25868': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01203444_240719'},
  '15490': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00894185_250817'},
  '09107': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00698863_020419'},
  '27033': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01235272_051115'},
  '11039': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00745209_220916'},
  '05939': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00626957_250216'},
  '07157': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00654671_151018'},
  '03735': {'GT': 0, 'Acc': ' 76.67%', 'Pred': [23, 0, 7], 'edfname': '00472015_120314'},
  '27224': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01238327_050819'},
  '10173': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00725683_091115'},
  '07538': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00664961_261218'},
  '18158': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00970997_301018'},
  '16072': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00907971_030217'},
  '30114': {'GT': 1, 'Acc': '  6.67%', 'Pred': [28, 2, 0], 'edfname': '01296342_141116'},
  '22673': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01139924_140717'},
  '22261': {'GT': 1, 'Acc': '  0.00%', 'Pred': [30, 0, 0], 'edfname': '01135545_230715'},
  '14330': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00853892_020519'},
  '27382': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01241947_301215'},
  '26062': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01209181_250315'},
  '03248': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '00416942_190516'},
  '23908': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01154140_240414'},
  '02484': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00354396_021116'},
  '07608': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00665821_260815'},
  '27937': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01252335_211016'},
  '16859': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [0, 29, 1], 'edfname': '00943284_210518'},
  '11448': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00758349_270418'},
  '05583': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00619387_070317'},
  '28548': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01266696_110516'},
  '23728': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01149087_170414'},
  '23182': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01141794_110214'},
  '34542': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01386864_220618'},
  '10256': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00729370_190214'},
  '13858': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00840062_080519'},
  '07531': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00664961_261218'},
  '00117': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00048377_070916'},
  '26569': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01220269_141218'},
  '31413': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01314894_190517'},
  '13133': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00809059_030715'},
  '13727': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00834803_280819'},
  '34024': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01370156_200218'},
  '17210': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00951808_251116'},
  '16419': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00925720_290414'},
  '05073': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00609255_140417'},
  '22229': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01135545_160117'},
  '26444': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01216443_240518'},
  '17810': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00959479_090217'},
  '14511': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00862802_260618'},
  '09630': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00713699_290318'},
  '26519': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01218042_121016'},
  '14371': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00856756_281119'},
  '30065': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01296102_150217'},
  '00941': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00209742_081217'},
  '32608': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01340109_030817'},
  '23031': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01141786_190214'},
  '21496': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01124522_170818'},
  '28150': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01254424_120118'},
  '29895': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01291791_100217'},
  '21556': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01126091_200515'},
  '15018': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00885321_170314'},
  '12585': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00788666_011118'},
  '17822': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00959479_090217'},
  '16396': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [5, 25, 0], 'edfname': '00919596_200117'},
  '20230': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01043928_210415'},
  '06497': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00640147_140519'},
  '04311': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00518633_311215'},
  '31093': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01307883_100217'},
  '17920': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00962086_270116'},
  '13483': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00817092_020118'},
  '19713': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01026990_241215'},
  '24748': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [3, 27, 0], 'edfname': '01174216_090516'},
  '27117': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 4, 0], 'edfname': '01235281_210915'},
  '34465': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01382697_261218'},
  '28391': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01264838_110416'},
  '17663': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00956561_241116'},
  '20524': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01064740_231214'},
  '15333': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00889595_240419'},
  '22255': {'GT': 1, 'Acc': ' 26.67%', 'Pred': [22, 8, 0], 'edfname': '01135545_230715'},
  '30251': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01296557_160120'},
  '08835': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00692685_110919'},
  '08729': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00687918_020318'},
  '20945': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01090425_290419'},
  '30129': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [3, 27, 0], 'edfname': '01296342_141116'},
  '34095': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01372947_240518'},
  '28477': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01264980_200516'},
  '20492': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01064650_161117'},
  '18695': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00986061_270116'},
  '18532': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00984999_070617'},
  '16966': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00948785_120116'},
  '19729': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01026990_241215'},
  '12469': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '00784417_100315'},
  '04286': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00507286_270715'},
  '08571': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00685191_100419'},
  '32107': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01334787_211117'},
  '29409': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01280762_171116'},
  '11460': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00758349_270418'},
  '09992': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00722143_261118'},
  '28686': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01269786_071019'},
  '00420': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00126875_260515'},
  '31013': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01306737_151216'},
  '01003': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00213418_210115'},
  '07285': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00657399_070515'},
  '21102': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01096498_270718'},
  '32014': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01333917_210918'},
  '08955': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00695058_191017'},
  '01995': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00315506_250715'},
  '16694': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00935598_301018'},
  '04320': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00518633_311215'},
  '11168': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00752045_260419'},
  '31307': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01312293_120417'},
  '21114': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01096498_270718'},
  '30581': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01301982_151116'}}

1D CNN: ResNet-like model

class BasicResBlock(nn.Module):
    expansion: int = 1

    def __init__(self, c_in, c_out, kernel_size, stride) -> None:
        super().__init__()
        self.conv1 = nn.Conv1d(in_channels=c_in, out_channels=c_out, 
                               kernel_size=kernel_size, stride=stride, 
                               padding=kernel_size//2, bias=False)
        self.bn1 = nn.BatchNorm1d(c_out)

        self.conv2 = nn.Conv1d(in_channels=c_out, out_channels=c_out, 
                               kernel_size=kernel_size, stride=1, 
                               padding=kernel_size//2, bias=False)
        self.bn2 = nn.BatchNorm1d(c_out)

        self.relu = nn.ReLU(inplace=True)
        
        self.downsample = None
        if stride != 1 or c_in != c_out:
            self.downsample = nn.Sequential(
                nn.Conv1d(in_channels=c_in, out_channels=c_out, 
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm1d(c_out)
            )
                    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        identity = x
        
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        
        x = self.conv2(x)
        x = self.bn2(x)
        if self.downsample is not None:
            identity = self.downsample(identity)
        x = self.relu(x + identity)
        
        return x

class BottleneckBlock(nn.Module):
    expansion: int = 4
        
    def __init__(self, c_in, c_out, kernel_size, stride) -> None:
        super().__init__()
        width = c_out
        self.conv1 = nn.Conv1d(in_channels=c_in, out_channels=width, 
                               kernel_size=1, stride=1, bias=False)
        self.bn1 = nn.BatchNorm1d(width)

        self.conv2 = nn.Conv1d(in_channels=width, out_channels=width, 
                               kernel_size=kernel_size, stride=stride, 
                               padding=kernel_size//2, bias=False)
        self.bn2 = nn.BatchNorm1d(width)

        self.conv3 = nn.Conv1d(in_channels=width, out_channels=c_out*self.expansion, 
                               kernel_size=1, stride=1, bias=False)
        self.bn3 = nn.BatchNorm1d(c_out*self.expansion)
        
        self.relu = nn.ReLU(inplace=True)
        
        self.downsample = None
        if stride != 1 or c_in != c_out*self.expansion:
            self.downsample = nn.Sequential(
                nn.Conv1d(in_channels=c_in, out_channels=c_out*self.expansion, 
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm1d(c_out*self.expansion)
            )
                    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        identity = x
        
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu(x)
        
        x = self.conv3(x)
        x = self.bn3(x)
        if self.downsample is not None:
            identity = self.downsample(identity)
            
        x = self.relu(x + identity)
        
        return x

class ResNet(nn.Module):
    def __init__(self, 
                 block: Type[Union[BasicResBlock, BottleneckBlock]], 
                 conv_layers: List[int],
                 n_fc: int,
                 n_input=20,
                 n_output=3,
                 n_start=64,
                 kernel_size=9, 
                 use_age=True, 
                 final_pool='average') -> None:
        
        super().__init__()
        
        if final_pool not in {'average', 'max'}:
            raise ValueError("final_pool must be set to one of ['average', 'max']")        

        self.c_current = n_start
        self.use_age = use_age
        
        self.input_stage = nn.Sequential(
            nn.Conv1d(in_channels=n_input, out_channels=n_start, 
                      kernel_size=kernel_size*3, stride=3,
                      padding=(kernel_size*3)//2, bias=False),
            nn.BatchNorm1d(n_start), 
            nn.ReLU(),
        )
        
        self.conv_stage1 = self._make_conv_layer(block, conv_layers[0], n_start, kernel_size, stride=3)
        self.conv_stage2 = self._make_conv_layer(block, conv_layers[1], n_start*2, kernel_size, stride=3)
        self.conv_stage3 = self._make_conv_layer(block, conv_layers[2], n_start*4, kernel_size, stride=3)
        self.conv_stage4 = self._make_conv_layer(block, conv_layers[3], n_start*8, kernel_size, stride=3)
        
        if final_pool == 'average':
            self.final_pool = nn.AdaptiveAvgPool1d(1)
        elif final_pool == 'max':
            self.final_pool = nn.AdaptiveMaxPool1d(1)
        
        fc_layers = []        
        if self.use_age:
            self.c_current = self.c_current + 1
        
        for l in range(n_fc):
            layer = nn.Sequential(nn.Linear(self.c_current, self.c_current // 2, bias=False),
                                  nn.Dropout(p=0.1),
                                  nn.BatchNorm1d(self.c_current // 2), 
                                  nn.ReLU())
            self.c_current = self.c_current // 2
            fc_layers.append(layer)
        fc_layers.append(nn.Linear(self.c_current, n_output))
        self.fc_stage = nn.Sequential(*fc_layers)
        
    def reset_weights(self):
        for m in self.modules():
            if hasattr(m, 'reset_parameters'):
                m.reset_parameters()
        
    def _make_conv_layer(self, block: Type[Union[BasicResBlock, BottleneckBlock]], 
                         n_block: int, c_out: int, kernel_size: int, stride: int = 1) -> nn.Sequential:
        layers = []
        c_in = self.c_current
        layers.append(block(c_in, c_out, kernel_size, stride=1))

        c_in = c_out * block.expansion
        self.c_current = c_in
        for _ in range(1, n_block):
            layers.append(block(c_in, c_out, kernel_size, stride=1))
            
        layers.append(nn.MaxPool1d(kernel_size=stride))
            
        return nn.Sequential(*layers)

    def forward(self, x, age):
        x = self.input_stage(x)
        
        x = self.conv_stage1(x)
        x = self.conv_stage2(x)
        x = self.conv_stage3(x)
        x = self.conv_stage4(x)
                
        x = self.final_pool(x).squeeze()
        if self.use_age:
            x = torch.cat((x, age.reshape(-1, 1)), dim=1)
        x = self.fc_stage(x)
        
        return x
        # return F.log_softmax(x, dim=2)

model = ResNet(block=BottleneckBlock, 
               conv_layers=[2, 2, 2, 2], 
               n_fc=3, 
               n_input=train_dataset[0]['signal'].shape[0], 
               n_output=3, 
               n_start=64,
               kernel_size=9, 
               use_age=True, 
               final_pool='max')
model = model.to(device, dtype=torch.float32)
print(model)
print()

# tensorboard visualization
visualize_network_tensorboard(model, 'ResNet-like')

# number of parameters
n = count_parameters(model)
print(f'The Number of parameters of the model: {n:,}')

ResNet(
  (input_stage): Sequential(
    (0): Conv1d(20, 64, kernel_size=(27,), stride=(3,), padding=(13,), bias=False)
    (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU()
  )
  (conv_stage1): Sequential(
    (0): BottleneckBlock(
      (conv1): Conv1d(64, 64, kernel_size=(1,), stride=(1,), bias=False)
      (bn1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv1d(64, 64, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)
      (bn2): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv1d(64, 256, kernel_size=(1,), stride=(1,), bias=False)
      (bn3): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (downsample): Sequential(
        (0): Conv1d(64, 256, kernel_size=(1,), stride=(1,), bias=False)
        (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): BottleneckBlock(
      (conv1): Conv1d(256, 64, kernel_size=(1,), stride=(1,), bias=False)
      (bn1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv1d(64, 64, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)
      (bn2): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv1d(64, 256, kernel_size=(1,), stride=(1,), bias=False)
      (bn3): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (2): MaxPool1d(kernel_size=3, stride=3, padding=0, dilation=1, ceil_mode=False)
  )
  (conv_stage2): Sequential(
    (0): BottleneckBlock(
      (conv1): Conv1d(256, 128, kernel_size=(1,), stride=(1,), bias=False)
      (bn1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv1d(128, 128, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)
      (bn2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv1d(128, 512, kernel_size=(1,), stride=(1,), bias=False)
      (bn3): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (downsample): Sequential(
        (0): Conv1d(256, 512, kernel_size=(1,), stride=(1,), bias=False)
        (1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): BottleneckBlock(
      (conv1): Conv1d(512, 128, kernel_size=(1,), stride=(1,), bias=False)
      (bn1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv1d(128, 128, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)
      (bn2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv1d(128, 512, kernel_size=(1,), stride=(1,), bias=False)
      (bn3): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (2): MaxPool1d(kernel_size=3, stride=3, padding=0, dilation=1, ceil_mode=False)
  )
  (conv_stage3): Sequential(
    (0): BottleneckBlock(
      (conv1): Conv1d(512, 256, kernel_size=(1,), stride=(1,), bias=False)
      (bn1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv1d(256, 256, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)
      (bn2): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv1d(256, 1024, kernel_size=(1,), stride=(1,), bias=False)
      (bn3): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (downsample): Sequential(
        (0): Conv1d(512, 1024, kernel_size=(1,), stride=(1,), bias=False)
        (1): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): BottleneckBlock(
      (conv1): Conv1d(1024, 256, kernel_size=(1,), stride=(1,), bias=False)
      (bn1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv1d(256, 256, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)
      (bn2): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv1d(256, 1024, kernel_size=(1,), stride=(1,), bias=False)
      (bn3): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (2): MaxPool1d(kernel_size=3, stride=3, padding=0, dilation=1, ceil_mode=False)
  )
  (conv_stage4): Sequential(
    (0): BottleneckBlock(
      (conv1): Conv1d(1024, 512, kernel_size=(1,), stride=(1,), bias=False)
      (bn1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv1d(512, 512, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)
      (bn2): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv1d(512, 2048, kernel_size=(1,), stride=(1,), bias=False)
      (bn3): BatchNorm1d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (downsample): Sequential(
        (0): Conv1d(1024, 2048, kernel_size=(1,), stride=(1,), bias=False)
        (1): BatchNorm1d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): BottleneckBlock(
      (conv1): Conv1d(2048, 512, kernel_size=(1,), stride=(1,), bias=False)
      (bn1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv1d(512, 512, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)
      (bn2): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv1d(512, 2048, kernel_size=(1,), stride=(1,), bias=False)
      (bn3): BatchNorm1d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (2): MaxPool1d(kernel_size=3, stride=3, padding=0, dilation=1, ceil_mode=False)
  )
  (final_pool): AdaptiveMaxPool1d(output_size=1)
  (fc_stage): Sequential(
    (0): Sequential(
      (0): Linear(in_features=2049, out_features=1024, bias=False)
      (1): Dropout(p=0.1, inplace=False)
      (2): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (3): ReLU()
    )
    (1): Sequential(
      (0): Linear(in_features=1024, out_features=512, bias=False)
      (1): Dropout(p=0.1, inplace=False)
      (2): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (3): ReLU()
    )
    (2): Sequential(
      (0): Linear(in_features=512, out_features=256, bias=False)
      (1): Dropout(p=0.1, inplace=False)
      (2): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (3): ReLU()
    )
    (3): Linear(in_features=256, out_features=3, bias=True)
  )
)

The Number of parameters of the model: 16,729,219

# record = learning_rate_search(model, 
#                               min_log_lr=-4.5, 
#                               max_log_lr=-1.0, 
#                               trials=500, 
#                               epochs=1)
# draw_learning_rate_record(record)
# best_log_lr = record[np.argmax(np.array([v for lr, v in record]))][0]

best_log_lr = -2.462123346845201
print('best_log_lr:', best_log_lr)

best_log_lr: -2.462123346845201

# reduce the learning after [lr_schedule_step] epochs by a factor of 10
n_epoch = 200
lr_schedule_step = 80
log_interval = len(train_loader) // 5

loss_history = []
train_acc_history = []
val_acc_history = []

model.reset_weights()
optimizer = optim.AdamW(model.parameters(), lr=10 ** best_log_lr, weight_decay=0.0001)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=lr_schedule_step, gamma=0.1)

print(f'{"*"*40} Training Starts {"*"*40}')

for epoch in tqdm(range(1, n_epoch + 1)):
    print(f'{"-"*30} Epoch {epoch:03d} / {n_epoch:03d}, Learning rate: {optimizer.param_groups[-1]["lr"]:.2e} {"-"*30}')
    
    # train
    loss, train_accuracy, train_confusion = train_one_epoch(model, optimizer, log_interval)
    loss_history.extend(loss)
    train_acc_history.append(train_accuracy)
    
    # validation
    val_accuracy, val_confusion = check_val_accuracy(model)
    val_acc_history.append(val_accuracy)
    
    # learning rate schedule
    scheduler.step()
    
    print()
    print(f'* Train accuracy / confusion: {train_accuracy:.2f}% / {train_confusion.tolist()}, ')
    print(f'* Val   accuracy / confusion: {val_accuracy:.2f}% / {val_confusion.tolist()}')
    print()
    
# test
test_accuracy, test_confusion, test_debug = check_test_accuracy(model, repeat=30)

print(f'{"*"*40} Training Ends {"*"*40}')
print(f'- Test accuracy: {test_accuracy:.2f}%')
print()
print('- Confusion matrix:\n', test_confusion)
print()

# draw the training loss plot
draw_loss_plot(loss_history)
draw_accuracy_history(train_acc_history, val_acc_history)

# draw the confusion matrix
draw_confusion(test_confusion)

**************************************** Training Starts ****************************************

------------------------------ Epoch 001 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 1.119428
- Iter 262 / 659, Loss: 1.135605
- Iter 393 / 659, Loss: 1.105717
- Iter 524 / 659, Loss: 1.094972
- Iter 655 / 659, Loss: 1.072527

* Train accuracy / confusion: 38.90% / [[5542, 2779, 225], [4725, 2552, 210], [3126, 1820, 109]], 
* Val   accuracy / confusion: 40.19% / [[821, 248, 0], [698, 240, 0], [456, 177, 0]]

------------------------------ Epoch 002 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 1.168470
- Iter 262 / 659, Loss: 1.103163
- Iter 393 / 659, Loss: 1.132564
- Iter 524 / 659, Loss: 1.047896
- Iter 655 / 659, Loss: 1.115005

* Train accuracy / confusion: 40.25% / [[7229, 1201, 113], [6215, 1151, 123], [4146, 803, 107]], 
* Val   accuracy / confusion: 41.17% / [[889, 146, 34], [738, 150, 50], [473, 112, 48]]

------------------------------ Epoch 003 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 1.080378
- Iter 262 / 659, Loss: 1.036138
- Iter 393 / 659, Loss: 1.062251
- Iter 524 / 659, Loss: 0.957155
- Iter 655 / 659, Loss: 0.987986

* Train accuracy / confusion: 43.52% / [[7015, 1332, 198], [5362, 1683, 448], [3184, 1386, 480]], 
* Val   accuracy / confusion: 51.25% / [[814, 255, 0], [399, 539, 0], [178, 455, 0]]

------------------------------ Epoch 004 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.932841
- Iter 262 / 659, Loss: 0.882407
- Iter 393 / 659, Loss: 0.907730
- Iter 524 / 659, Loss: 0.877315
- Iter 655 / 659, Loss: 1.009224

* Train accuracy / confusion: 51.44% / [[6130, 2170, 240], [2926, 3753, 816], [1228, 2861, 964]], 
* Val   accuracy / confusion: 54.51% / [[844, 205, 20], [365, 434, 139], [151, 321, 161]]

------------------------------ Epoch 005 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.852597
- Iter 262 / 659, Loss: 0.969685
- Iter 393 / 659, Loss: 0.939616
- Iter 524 / 659, Loss: 1.041442
- Iter 655 / 659, Loss: 0.872671

* Train accuracy / confusion: 53.86% / [[6108, 2021, 413], [2595, 3445, 1450], [796, 2455, 1805]], 
* Val   accuracy / confusion: 52.80% / [[659, 283, 127], [204, 327, 407], [43, 182, 408]]

------------------------------ Epoch 006 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.974099
- Iter 262 / 659, Loss: 1.100408
- Iter 393 / 659, Loss: 0.928434
- Iter 524 / 659, Loss: 0.768112
- Iter 655 / 659, Loss: 0.812360

* Train accuracy / confusion: 55.19% / [[6125, 1996, 425], [2528, 3292, 1665], [662, 2174, 2221]], 
* Val   accuracy / confusion: 55.00% / [[638, 324, 107], [177, 373, 388], [33, 159, 441]]

------------------------------ Epoch 007 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.992249
- Iter 262 / 659, Loss: 0.928885
- Iter 393 / 659, Loss: 0.764910
- Iter 524 / 659, Loss: 0.973467
- Iter 655 / 659, Loss: 0.782583

* Train accuracy / confusion: 56.68% / [[6215, 1956, 373], [2575, 3290, 1625], [617, 1989, 2448]], 
* Val   accuracy / confusion: 55.61% / [[936, 129, 4], [460, 425, 53], [181, 345, 107]]

------------------------------ Epoch 008 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 1.024740
- Iter 262 / 659, Loss: 0.859558
- Iter 393 / 659, Loss: 0.854620
- Iter 524 / 659, Loss: 0.904514
- Iter 655 / 659, Loss: 0.915367

* Train accuracy / confusion: 57.76% / [[6315, 1864, 362], [2514, 3473, 1506], [568, 2094, 2392]], 
* Val   accuracy / confusion: 57.12% / [[937, 127, 5], [444, 434, 60], [186, 310, 137]]

------------------------------ Epoch 009 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.760216
- Iter 262 / 659, Loss: 0.846102
- Iter 393 / 659, Loss: 0.817047
- Iter 524 / 659, Loss: 0.876912
- Iter 655 / 659, Loss: 0.842156

* Train accuracy / confusion: 58.50% / [[6338, 1855, 349], [2473, 3555, 1460], [558, 2056, 2444]], 
* Val   accuracy / confusion: 57.50% / [[691, 369, 9], [196, 675, 67], [46, 435, 152]]

------------------------------ Epoch 010 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 1.102282
- Iter 262 / 659, Loss: 0.811027
- Iter 393 / 659, Loss: 0.913108
- Iter 524 / 659, Loss: 1.010753
- Iter 655 / 659, Loss: 0.935065

* Train accuracy / confusion: 59.19% / [[6375, 1825, 346], [2482, 3617, 1388], [555, 2010, 2490]], 
* Val   accuracy / confusion: 58.60% / [[911, 154, 4], [401, 464, 73], [173, 288, 172]]

------------------------------ Epoch 011 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.809250
- Iter 262 / 659, Loss: 0.895870
- Iter 393 / 659, Loss: 0.790049
- Iter 524 / 659, Loss: 0.741542
- Iter 655 / 659, Loss: 0.797443

* Train accuracy / confusion: 59.82% / [[6374, 1801, 368], [2400, 3639, 1451], [534, 1919, 2602]], 
* Val   accuracy / confusion: 58.07% / [[830, 233, 6], [297, 607, 34], [94, 443, 96]]

------------------------------ Epoch 012 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.924912
- Iter 262 / 659, Loss: 0.730432
- Iter 393 / 659, Loss: 0.758567
- Iter 524 / 659, Loss: 0.774739
- Iter 655 / 659, Loss: 0.759144

* Train accuracy / confusion: 60.54% / [[6372, 1807, 365], [2318, 3736, 1435], [494, 1903, 2658]], 
* Val   accuracy / confusion: 61.44% / [[896, 154, 19], [356, 447, 135], [109, 245, 279]]

------------------------------ Epoch 013 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.685209
- Iter 262 / 659, Loss: 0.751859
- Iter 393 / 659, Loss: 0.637252
- Iter 524 / 659, Loss: 0.912792
- Iter 655 / 659, Loss: 0.750927

* Train accuracy / confusion: 61.33% / [[6342, 1843, 356], [2225, 3865, 1400], [503, 1827, 2727]], 
* Val   accuracy / confusion: 61.21% / [[708, 324, 37], [162, 635, 141], [41, 319, 273]]

------------------------------ Epoch 014 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.679441
- Iter 262 / 659, Loss: 0.701015
- Iter 393 / 659, Loss: 0.680421
- Iter 524 / 659, Loss: 0.684285
- Iter 655 / 659, Loss: 0.871045

* Train accuracy / confusion: 61.78% / [[6411, 1784, 350], [2295, 3731, 1460], [514, 1657, 2886]], 
* Val   accuracy / confusion: 60.98% / [[914, 152, 3], [358, 516, 64], [144, 309, 180]]

------------------------------ Epoch 015 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.900626
- Iter 262 / 659, Loss: 0.804273
- Iter 393 / 659, Loss: 0.793366
- Iter 524 / 659, Loss: 0.829680
- Iter 655 / 659, Loss: 0.728354

* Train accuracy / confusion: 63.38% / [[6448, 1736, 356], [2208, 3886, 1399], [476, 1547, 3032]], 
* Val   accuracy / confusion: 60.87% / [[731, 320, 18], [181, 690, 67], [67, 380, 186]]

------------------------------ Epoch 016 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.691360
- Iter 262 / 659, Loss: 0.659436
- Iter 393 / 659, Loss: 0.672233
- Iter 524 / 659, Loss: 0.938226
- Iter 655 / 659, Loss: 0.864497

* Train accuracy / confusion: 63.88% / [[6418, 1760, 365], [2146, 3949, 1397], [484, 1465, 3104]], 
* Val   accuracy / confusion: 60.45% / [[732, 323, 14], [192, 696, 50], [46, 419, 168]]

------------------------------ Epoch 017 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.813926
- Iter 262 / 659, Loss: 0.829730
- Iter 393 / 659, Loss: 0.687276
- Iter 524 / 659, Loss: 0.623700
- Iter 655 / 659, Loss: 0.822578

* Train accuracy / confusion: 65.15% / [[6492, 1684, 366], [2116, 4056, 1320], [456, 1408, 3190]], 
* Val   accuracy / confusion: 63.30% / [[756, 278, 35], [213, 617, 108], [54, 281, 298]]

------------------------------ Epoch 018 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.930872
- Iter 262 / 659, Loss: 0.933936
- Iter 393 / 659, Loss: 0.633741
- Iter 524 / 659, Loss: 0.548340
- Iter 655 / 659, Loss: 0.659513

* Train accuracy / confusion: 65.27% / [[6477, 1697, 371], [2106, 4037, 1345], [451, 1354, 3250]], 
* Val   accuracy / confusion: 64.73% / [[815, 179, 75], [255, 440, 243], [66, 113, 454]]

------------------------------ Epoch 019 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.663260
- Iter 262 / 659, Loss: 0.693257
- Iter 393 / 659, Loss: 0.665145
- Iter 524 / 659, Loss: 1.105252
- Iter 655 / 659, Loss: 0.790574

* Train accuracy / confusion: 66.57% / [[6500, 1662, 382], [2043, 4195, 1251], [410, 1301, 3344]], 
* Val   accuracy / confusion: 59.28% / [[934, 29, 106], [431, 118, 389], [100, 20, 513]]

------------------------------ Epoch 020 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.776520
- Iter 262 / 659, Loss: 0.797274
- Iter 393 / 659, Loss: 0.778444
- Iter 524 / 659, Loss: 0.723741
- Iter 655 / 659, Loss: 0.749961

* Train accuracy / confusion: 67.33% / [[6485, 1679, 379], [1987, 4297, 1205], [397, 1242, 3417]], 
* Val   accuracy / confusion: 65.23% / [[928, 97, 44], [382, 332, 224], [111, 60, 462]]

------------------------------ Epoch 021 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.687803
- Iter 262 / 659, Loss: 0.731387
- Iter 393 / 659, Loss: 0.497932
- Iter 524 / 659, Loss: 0.834266
- Iter 655 / 659, Loss: 0.645041

* Train accuracy / confusion: 68.71% / [[6506, 1687, 348], [1812, 4538, 1140], [381, 1230, 3446]], 
* Val   accuracy / confusion: 65.83% / [[889, 176, 4], [296, 582, 60], [125, 241, 267]]

------------------------------ Epoch 022 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.782297
- Iter 262 / 659, Loss: 0.777735
- Iter 393 / 659, Loss: 0.684926
- Iter 524 / 659, Loss: 0.752137
- Iter 655 / 659, Loss: 0.668597

* Train accuracy / confusion: 69.87% / [[6664, 1539, 339], [1860, 4559, 1073], [411, 1131, 3512]], 
* Val   accuracy / confusion: 69.09% / [[828, 194, 47], [217, 599, 122], [74, 162, 397]]

------------------------------ Epoch 023 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.667878
- Iter 262 / 659, Loss: 0.642465
- Iter 393 / 659, Loss: 0.670283
- Iter 524 / 659, Loss: 0.545565
- Iter 655 / 659, Loss: 0.740908

* Train accuracy / confusion: 71.84% / [[6770, 1411, 359], [1713, 4776, 1004], [402, 1050, 3603]], 
* Val   accuracy / confusion: 65.72% / [[1005, 62, 2], [455, 426, 57], [203, 126, 304]]

------------------------------ Epoch 024 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.610935
- Iter 262 / 659, Loss: 0.555377
- Iter 393 / 659, Loss: 0.936297
- Iter 524 / 659, Loss: 0.598204
- Iter 655 / 659, Loss: 0.627406

* Train accuracy / confusion: 73.65% / [[6841, 1362, 337], [1642, 5017, 833], [359, 1023, 3674]], 
* Val   accuracy / confusion: 72.23% / [[849, 132, 88], [199, 543, 196], [43, 75, 515]]

------------------------------ Epoch 025 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.552284
- Iter 262 / 659, Loss: 0.507553
- Iter 393 / 659, Loss: 0.607802
- Iter 524 / 659, Loss: 0.572585
- Iter 655 / 659, Loss: 0.383709

* Train accuracy / confusion: 75.43% / [[6999, 1228, 316], [1587, 5121, 782], [369, 899, 3787]], 
* Val   accuracy / confusion: 60.45% / [[392, 656, 21], [25, 888, 25], [4, 313, 316]]

------------------------------ Epoch 026 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.866932
- Iter 262 / 659, Loss: 0.451368
- Iter 393 / 659, Loss: 0.673117
- Iter 524 / 659, Loss: 0.443061
- Iter 655 / 659, Loss: 0.646060

* Train accuracy / confusion: 76.93% / [[7056, 1209, 278], [1453, 5287, 754], [329, 841, 3881]], 
* Val   accuracy / confusion: 77.05% / [[931, 120, 18], [186, 670, 82], [78, 122, 433]]

------------------------------ Epoch 027 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.488402
- Iter 262 / 659, Loss: 0.297945
- Iter 393 / 659, Loss: 0.483753
- Iter 524 / 659, Loss: 0.585274
- Iter 655 / 659, Loss: 0.620042

* Train accuracy / confusion: 78.88% / [[7155, 1080, 304], [1335, 5437, 724], [330, 681, 4042]], 
* Val   accuracy / confusion: 78.48% / [[899, 124, 46], [158, 638, 142], [50, 48, 535]]

------------------------------ Epoch 028 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.387215
- Iter 262 / 659, Loss: 0.491680
- Iter 393 / 659, Loss: 0.345747
- Iter 524 / 659, Loss: 0.995771
- Iter 655 / 659, Loss: 0.281255

* Train accuracy / confusion: 81.20% / [[7314, 960, 273], [1191, 5652, 643], [282, 616, 4157]], 
* Val   accuracy / confusion: 73.45% / [[875, 56, 138], [152, 454, 332], [16, 7, 610]]

------------------------------ Epoch 029 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.456591
- Iter 262 / 659, Loss: 0.509541
- Iter 393 / 659, Loss: 0.255700
- Iter 524 / 659, Loss: 0.342563
- Iter 655 / 659, Loss: 0.216646

* Train accuracy / confusion: 82.62% / [[7373, 916, 259], [1067, 5857, 562], [283, 579, 4192]], 
* Val   accuracy / confusion: 81.70% / [[897, 134, 38], [105, 750, 83], [54, 69, 510]]

------------------------------ Epoch 030 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.428430
- Iter 262 / 659, Loss: 0.368410
- Iter 393 / 659, Loss: 0.369234
- Iter 524 / 659, Loss: 0.446704
- Iter 655 / 659, Loss: 0.277098

* Train accuracy / confusion: 84.28% / [[7497, 792, 251], [988, 5996, 507], [282, 495, 4280]], 
* Val   accuracy / confusion: 82.54% / [[856, 127, 86], [76, 731, 131], [23, 18, 592]]

------------------------------ Epoch 031 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.410216
- Iter 262 / 659, Loss: 0.361617
- Iter 393 / 659, Loss: 0.405560
- Iter 524 / 659, Loss: 0.170244
- Iter 655 / 659, Loss: 0.352587

* Train accuracy / confusion: 86.23% / [[7612, 700, 228], [873, 6193, 426], [255, 421, 4380]], 
* Val   accuracy / confusion: 86.29% / [[999, 58, 12], [129, 783, 26], [42, 95, 496]]

------------------------------ Epoch 032 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.287599
- Iter 262 / 659, Loss: 0.272146
- Iter 393 / 659, Loss: 0.305034
- Iter 524 / 659, Loss: 0.300014
- Iter 655 / 659, Loss: 0.443792

* Train accuracy / confusion: 87.69% / [[7735, 573, 239], [788, 6310, 389], [218, 389, 4447]], 
* Val   accuracy / confusion: 86.06% / [[946, 118, 5], [61, 867, 10], [47, 127, 459]]

------------------------------ Epoch 033 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.744316
- Iter 262 / 659, Loss: 0.297672
- Iter 393 / 659, Loss: 0.188624
- Iter 524 / 659, Loss: 0.391379
- Iter 655 / 659, Loss: 0.771254

* Train accuracy / confusion: 88.77% / [[7775, 559, 206], [693, 6448, 352], [211, 347, 4497]], 
* Val   accuracy / confusion: 88.33% / [[1045, 18, 6], [153, 716, 69], [43, 19, 571]]

------------------------------ Epoch 034 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.158494
- Iter 262 / 659, Loss: 0.247450
- Iter 393 / 659, Loss: 0.195624
- Iter 524 / 659, Loss: 0.234822
- Iter 655 / 659, Loss: 0.082902

* Train accuracy / confusion: 90.18% / [[7893, 474, 176], [608, 6573, 311], [199, 302, 4552]], 
* Val   accuracy / confusion: 90.00% / [[999, 61, 9], [92, 814, 32], [44, 26, 563]]

------------------------------ Epoch 035 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.206880
- Iter 262 / 659, Loss: 0.534804
- Iter 393 / 659, Loss: 0.225727
- Iter 524 / 659, Loss: 0.118502
- Iter 655 / 659, Loss: 0.262863

* Train accuracy / confusion: 91.09% / [[7931, 444, 172], [558, 6641, 285], [173, 246, 4638]], 
* Val   accuracy / confusion: 90.80% / [[982, 77, 10], [49, 849, 40], [21, 46, 566]]

------------------------------ Epoch 036 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.325840
- Iter 262 / 659, Loss: 0.298931
- Iter 393 / 659, Loss: 0.492724
- Iter 524 / 659, Loss: 0.187755
- Iter 655 / 659, Loss: 0.284641

* Train accuracy / confusion: 91.89% / [[7994, 406, 142], [554, 6693, 243], [143, 223, 4690]], 
* Val   accuracy / confusion: 90.38% / [[967, 93, 9], [42, 879, 17], [33, 60, 540]]

------------------------------ Epoch 037 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.347460
- Iter 262 / 659, Loss: 0.443971
- Iter 393 / 659, Loss: 0.358192
- Iter 524 / 659, Loss: 0.078390
- Iter 655 / 659, Loss: 0.267774

* Train accuracy / confusion: 92.60% / [[8062, 347, 131], [505, 6758, 230], [134, 214, 4707]], 
* Val   accuracy / confusion: 91.02% / [[1016, 50, 3], [67, 864, 7], [63, 47, 523]]

------------------------------ Epoch 038 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.056580
- Iter 262 / 659, Loss: 0.066512
- Iter 393 / 659, Loss: 0.464005
- Iter 524 / 659, Loss: 0.057850
- Iter 655 / 659, Loss: 0.126941

* Train accuracy / confusion: 93.17% / [[8077, 344, 123], [479, 6829, 187], [133, 175, 4741]], 
* Val   accuracy / confusion: 92.42% / [[1027, 41, 1], [56, 861, 21], [50, 31, 552]]

------------------------------ Epoch 039 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.200474
- Iter 262 / 659, Loss: 0.173319
- Iter 393 / 659, Loss: 0.370977
- Iter 524 / 659, Loss: 0.042937
- Iter 655 / 659, Loss: 0.208583

* Train accuracy / confusion: 93.64% / [[8127, 313, 100], [421, 6876, 195], [119, 193, 4744]], 
* Val   accuracy / confusion: 92.95% / [[974, 82, 13], [27, 891, 20], [11, 33, 589]]

------------------------------ Epoch 040 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.359247
- Iter 262 / 659, Loss: 0.341665
- Iter 393 / 659, Loss: 0.026060
- Iter 524 / 659, Loss: 0.121676
- Iter 655 / 659, Loss: 0.130487

* Train accuracy / confusion: 94.43% / [[8199, 231, 110], [408, 6930, 152], [124, 149, 4785]], 
* Val   accuracy / confusion: 93.52% / [[1009, 54, 6], [38, 890, 10], [25, 38, 570]]

------------------------------ Epoch 041 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.236285
- Iter 262 / 659, Loss: 0.115398
- Iter 393 / 659, Loss: 0.286138
- Iter 524 / 659, Loss: 0.062261
- Iter 655 / 659, Loss: 0.045166

* Train accuracy / confusion: 94.53% / [[8159, 272, 109], [359, 6972, 159], [111, 144, 4803]], 
* Val   accuracy / confusion: 94.58% / [[1018, 37, 14], [36, 867, 35], [9, 12, 612]]

------------------------------ Epoch 042 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.105824
- Iter 262 / 659, Loss: 0.135432
- Iter 393 / 659, Loss: 0.172486
- Iter 524 / 659, Loss: 0.085190
- Iter 655 / 659, Loss: 0.106756

* Train accuracy / confusion: 94.65% / [[8194, 244, 108], [393, 6962, 138], [100, 146, 4803]], 
* Val   accuracy / confusion: 94.20% / [[1042, 22, 5], [55, 855, 28], [33, 10, 590]]

------------------------------ Epoch 043 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.147657
- Iter 262 / 659, Loss: 0.061690
- Iter 393 / 659, Loss: 0.037203
- Iter 524 / 659, Loss: 0.316051
- Iter 655 / 659, Loss: 0.431592

* Train accuracy / confusion: 95.15% / [[8221, 243, 83], [336, 7016, 137], [95, 128, 4829]], 
* Val   accuracy / confusion: 94.39% / [[1038, 23, 8], [59, 850, 29], [21, 8, 604]]

------------------------------ Epoch 044 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.211137
- Iter 262 / 659, Loss: 0.066028
- Iter 393 / 659, Loss: 0.264729
- Iter 524 / 659, Loss: 0.145963
- Iter 655 / 659, Loss: 0.213815

* Train accuracy / confusion: 95.42% / [[8240, 220, 82], [319, 7048, 124], [88, 133, 4834]], 
* Val   accuracy / confusion: 91.40% / [[934, 129, 6], [23, 911, 4], [8, 57, 568]]

------------------------------ Epoch 045 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.022656
- Iter 262 / 659, Loss: 0.038606
- Iter 393 / 659, Loss: 0.136560
- Iter 524 / 659, Loss: 0.063097
- Iter 655 / 659, Loss: 0.046585

* Train accuracy / confusion: 95.64% / [[8263, 204, 74], [313, 7060, 118], [85, 125, 4846]], 
* Val   accuracy / confusion: 94.66% / [[1048, 20, 1], [51, 881, 6], [36, 27, 570]]

------------------------------ Epoch 046 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.020313
- Iter 262 / 659, Loss: 0.160774
- Iter 393 / 659, Loss: 0.098429
- Iter 524 / 659, Loss: 0.117862
- Iter 655 / 659, Loss: 0.020950

* Train accuracy / confusion: 96.00% / [[8256, 205, 88], [264, 7116, 107], [88, 92, 4872]], 
* Val   accuracy / confusion: 96.17% / [[1053, 12, 4], [47, 883, 8], [23, 7, 603]]

------------------------------ Epoch 047 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.037605
- Iter 262 / 659, Loss: 0.161595
- Iter 393 / 659, Loss: 0.014645
- Iter 524 / 659, Loss: 0.166611
- Iter 655 / 659, Loss: 0.043163

* Train accuracy / confusion: 96.10% / [[8263, 196, 83], [267, 7120, 102], [81, 93, 4883]], 
* Val   accuracy / confusion: 96.33% / [[1049, 14, 6], [37, 891, 10], [24, 6, 603]]

------------------------------ Epoch 048 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.124779
- Iter 262 / 659, Loss: 0.049062
- Iter 393 / 659, Loss: 0.021460
- Iter 524 / 659, Loss: 0.244343
- Iter 655 / 659, Loss: 0.023966

* Train accuracy / confusion: 96.07% / [[8270, 192, 74], [260, 7121, 115], [82, 105, 4869]], 
* Val   accuracy / confusion: 94.92% / [[1022, 44, 3], [24, 913, 1], [15, 47, 571]]

------------------------------ Epoch 049 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.151666
- Iter 262 / 659, Loss: 0.053316
- Iter 393 / 659, Loss: 0.025190
- Iter 524 / 659, Loss: 0.031345
- Iter 655 / 659, Loss: 0.103349

* Train accuracy / confusion: 96.46% / [[8296, 181, 64], [239, 7155, 96], [66, 100, 4891]], 
* Val   accuracy / confusion: 96.93% / [[1063, 5, 1], [34, 894, 10], [27, 4, 602]]

------------------------------ Epoch 050 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.082015
- Iter 262 / 659, Loss: 0.171895
- Iter 393 / 659, Loss: 0.019044
- Iter 524 / 659, Loss: 0.013048
- Iter 655 / 659, Loss: 0.089293

* Train accuracy / confusion: 96.85% / [[8323, 162, 56], [206, 7194, 95], [63, 82, 4907]], 
* Val   accuracy / confusion: 96.55% / [[1055, 9, 5], [33, 898, 7], [19, 18, 596]]

------------------------------ Epoch 051 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.036004
- Iter 262 / 659, Loss: 0.120133
- Iter 393 / 659, Loss: 0.115007
- Iter 524 / 659, Loss: 0.007397
- Iter 655 / 659, Loss: 0.104065

* Train accuracy / confusion: 96.54% / [[8305, 161, 77], [223, 7160, 107], [63, 98, 4894]], 
* Val   accuracy / confusion: 97.01% / [[1060, 7, 2], [37, 892, 9], [12, 12, 609]]

------------------------------ Epoch 052 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.039353
- Iter 262 / 659, Loss: 0.020830
- Iter 393 / 659, Loss: 0.194217
- Iter 524 / 659, Loss: 0.018772
- Iter 655 / 659, Loss: 0.031430

* Train accuracy / confusion: 96.91% / [[8319, 155, 68], [200, 7200, 89], [57, 83, 4917]], 
* Val   accuracy / confusion: 93.18% / [[929, 114, 26], [6, 912, 20], [1, 13, 619]]

------------------------------ Epoch 053 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.019507
- Iter 262 / 659, Loss: 0.362056
- Iter 393 / 659, Loss: 0.023961
- Iter 524 / 659, Loss: 0.059682
- Iter 655 / 659, Loss: 0.132819

* Train accuracy / confusion: 96.67% / [[8320, 157, 68], [209, 7176, 105], [64, 100, 4889]], 
* Val   accuracy / confusion: 95.83% / [[1061, 7, 1], [54, 874, 10], [33, 5, 595]]

------------------------------ Epoch 054 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.047770
- Iter 262 / 659, Loss: 0.103590
- Iter 393 / 659, Loss: 0.145929
- Iter 524 / 659, Loss: 0.077783
- Iter 655 / 659, Loss: 0.230737

* Train accuracy / confusion: 97.01% / [[8339, 150, 56], [193, 7198, 97], [67, 67, 4921]], 
* Val   accuracy / confusion: 96.70% / [[1047, 18, 4], [31, 901, 6], [10, 18, 605]]

------------------------------ Epoch 055 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.048125
- Iter 262 / 659, Loss: 0.017791
- Iter 393 / 659, Loss: 0.033986
- Iter 524 / 659, Loss: 0.020077
- Iter 655 / 659, Loss: 0.090136

* Train accuracy / confusion: 97.15% / [[8345, 138, 55], [188, 7223, 80], [57, 83, 4919]], 
* Val   accuracy / confusion: 94.17% / [[1043, 19, 7], [45, 838, 55], [22, 6, 605]]

------------------------------ Epoch 056 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.008515
- Iter 262 / 659, Loss: 0.045817
- Iter 393 / 659, Loss: 0.008133
- Iter 524 / 659, Loss: 0.011291
- Iter 655 / 659, Loss: 0.050746

* Train accuracy / confusion: 97.00% / [[8335, 155, 51], [189, 7208, 96], [61, 80, 4913]], 
* Val   accuracy / confusion: 95.76% / [[1060, 3, 6], [44, 893, 1], [36, 22, 575]]

------------------------------ Epoch 057 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.032956
- Iter 262 / 659, Loss: 0.155064
- Iter 393 / 659, Loss: 0.158278
- Iter 524 / 659, Loss: 0.211497
- Iter 655 / 659, Loss: 0.078310

* Train accuracy / confusion: 97.27% / [[8345, 149, 52], [180, 7241, 70], [50, 74, 4927]], 
* Val   accuracy / confusion: 96.59% / [[1058, 11, 0], [48, 886, 4], [20, 7, 606]]

------------------------------ Epoch 058 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.040338
- Iter 262 / 659, Loss: 0.017626
- Iter 393 / 659, Loss: 0.059146
- Iter 524 / 659, Loss: 0.019270
- Iter 655 / 659, Loss: 0.070091

* Train accuracy / confusion: 97.49% / [[8349, 139, 56], [160, 7265, 67], [55, 53, 4944]], 
* Val   accuracy / confusion: 97.20% / [[1047, 16, 6], [23, 907, 8], [12, 9, 612]]

------------------------------ Epoch 059 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.029222
- Iter 262 / 659, Loss: 0.026981
- Iter 393 / 659, Loss: 0.106305
- Iter 524 / 659, Loss: 0.006248
- Iter 655 / 659, Loss: 0.138253

* Train accuracy / confusion: 97.45% / [[8372, 125, 46], [163, 7244, 81], [51, 72, 4934]], 
* Val   accuracy / confusion: 96.25% / [[1051, 14, 4], [43, 883, 12], [20, 6, 607]]

------------------------------ Epoch 060 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.005947
- Iter 262 / 659, Loss: 0.010225
- Iter 393 / 659, Loss: 0.076147
- Iter 524 / 659, Loss: 0.030676
- Iter 655 / 659, Loss: 0.002490

* Train accuracy / confusion: 97.62% / [[8370, 125, 45], [150, 7272, 69], [52, 61, 4944]], 
* Val   accuracy / confusion: 97.08% / [[1044, 12, 13], [22, 904, 12], [9, 9, 615]]

------------------------------ Epoch 061 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.195652
- Iter 262 / 659, Loss: 0.052108
- Iter 393 / 659, Loss: 0.053565
- Iter 524 / 659, Loss: 0.079239
- Iter 655 / 659, Loss: 0.023243

* Train accuracy / confusion: 97.76% / [[8403, 101, 37], [140, 7277, 74], [47, 73, 4936]], 
* Val   accuracy / confusion: 96.86% / [[1048, 19, 2], [26, 904, 8], [11, 17, 605]]

------------------------------ Epoch 062 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.044916
- Iter 262 / 659, Loss: 0.014013
- Iter 393 / 659, Loss: 0.042524
- Iter 524 / 659, Loss: 0.028369
- Iter 655 / 659, Loss: 0.030995

* Train accuracy / confusion: 97.86% / [[8389, 112, 42], [137, 7291, 59], [42, 59, 4957]], 
* Val   accuracy / confusion: 97.12% / [[1064, 4, 1], [37, 896, 5], [19, 10, 604]]

------------------------------ Epoch 063 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.096429
- Iter 262 / 659, Loss: 0.036743
- Iter 393 / 659, Loss: 0.016205
- Iter 524 / 659, Loss: 0.008068
- Iter 655 / 659, Loss: 0.043368

* Train accuracy / confusion: 97.67% / [[8365, 132, 47], [147, 7289, 56], [52, 57, 4943]], 
* Val   accuracy / confusion: 97.73% / [[1051, 13, 5], [18, 918, 2], [13, 9, 611]]

------------------------------ Epoch 064 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.025397
- Iter 262 / 659, Loss: 0.021614
- Iter 393 / 659, Loss: 0.006266
- Iter 524 / 659, Loss: 0.137906
- Iter 655 / 659, Loss: 0.033959

* Train accuracy / confusion: 97.74% / [[8398, 90, 51], [132, 7292, 71], [53, 80, 4921]], 
* Val   accuracy / confusion: 97.16% / [[1038, 28, 3], [12, 917, 9], [13, 10, 610]]

------------------------------ Epoch 065 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.022757
- Iter 262 / 659, Loss: 0.046408
- Iter 393 / 659, Loss: 0.037929
- Iter 524 / 659, Loss: 0.054217
- Iter 655 / 659, Loss: 0.134098

* Train accuracy / confusion: 98.07% / [[8410, 97, 37], [119, 7303, 63], [42, 48, 4969]], 
* Val   accuracy / confusion: 97.42% / [[1049, 18, 2], [15, 920, 3], [16, 14, 603]]

------------------------------ Epoch 066 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.399983
- Iter 262 / 659, Loss: 0.053138
- Iter 393 / 659, Loss: 0.024030
- Iter 524 / 659, Loss: 0.005651
- Iter 655 / 659, Loss: 0.142589

* Train accuracy / confusion: 97.73% / [[8372, 130, 38], [148, 7278, 65], [51, 46, 4960]], 
* Val   accuracy / confusion: 97.46% / [[1053, 14, 2], [24, 906, 8], [15, 4, 614]]

------------------------------ Epoch 067 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.395509
- Iter 262 / 659, Loss: 0.224513
- Iter 393 / 659, Loss: 0.073375
- Iter 524 / 659, Loss: 0.055726
- Iter 655 / 659, Loss: 0.084554

* Train accuracy / confusion: 98.06% / [[8407, 100, 35], [128, 7302, 60], [41, 46, 4969]], 
* Val   accuracy / confusion: 98.26% / [[1060, 6, 3], [15, 914, 9], [10, 3, 620]]

------------------------------ Epoch 068 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.213153
- Iter 262 / 659, Loss: 0.007608
- Iter 393 / 659, Loss: 0.012910
- Iter 524 / 659, Loss: 0.003700
- Iter 655 / 659, Loss: 0.005731

* Train accuracy / confusion: 98.23% / [[8417, 92, 35], [120, 7319, 52], [32, 43, 4978]], 
* Val   accuracy / confusion: 97.12% / [[1032, 27, 10], [12, 923, 3], [12, 12, 609]]

------------------------------ Epoch 069 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.005235
- Iter 262 / 659, Loss: 0.003563
- Iter 393 / 659, Loss: 0.012255
- Iter 524 / 659, Loss: 0.201339
- Iter 655 / 659, Loss: 0.289441

* Train accuracy / confusion: 97.93% / [[8388, 114, 43], [125, 7305, 61], [40, 53, 4959]], 
* Val   accuracy / confusion: 97.61% / [[1061, 7, 1], [28, 904, 6], [14, 7, 612]]

------------------------------ Epoch 070 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.175532
- Iter 262 / 659, Loss: 0.062896
- Iter 393 / 659, Loss: 0.038898
- Iter 524 / 659, Loss: 0.055745
- Iter 655 / 659, Loss: 0.019410

* Train accuracy / confusion: 98.17% / [[8424, 82, 34], [98, 7327, 69], [45, 57, 4952]], 
* Val   accuracy / confusion: 96.67% / [[1032, 35, 2], [16, 915, 7], [18, 10, 605]]

------------------------------ Epoch 071 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.025636
- Iter 262 / 659, Loss: 0.007865
- Iter 393 / 659, Loss: 0.015169
- Iter 524 / 659, Loss: 0.079583
- Iter 655 / 659, Loss: 0.048751

* Train accuracy / confusion: 98.23% / [[8414, 87, 41], [104, 7339, 51], [45, 45, 4962]], 
* Val   accuracy / confusion: 97.95% / [[1046, 14, 9], [12, 918, 8], [3, 8, 622]]

------------------------------ Epoch 072 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.061924
- Iter 262 / 659, Loss: 0.127452
- Iter 393 / 659, Loss: 0.114667
- Iter 524 / 659, Loss: 0.045765
- Iter 655 / 659, Loss: 0.002618

* Train accuracy / confusion: 98.34% / [[8421, 96, 29], [103, 7332, 53], [28, 42, 4984]], 
* Val   accuracy / confusion: 95.83% / [[1018, 46, 5], [6, 929, 3], [6, 44, 583]]

------------------------------ Epoch 073 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.020018
- Iter 262 / 659, Loss: 0.315160
- Iter 393 / 659, Loss: 0.003790
- Iter 524 / 659, Loss: 0.177496
- Iter 655 / 659, Loss: 0.002799

* Train accuracy / confusion: 98.18% / [[8406, 96, 45], [114, 7324, 49], [39, 41, 4974]], 
* Val   accuracy / confusion: 98.03% / [[1057, 7, 5], [22, 909, 7], [5, 6, 622]]

------------------------------ Epoch 074 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.008602
- Iter 262 / 659, Loss: 0.058291
- Iter 393 / 659, Loss: 0.006959
- Iter 524 / 659, Loss: 0.180754
- Iter 655 / 659, Loss: 0.068855

* Train accuracy / confusion: 98.16% / [[8417, 95, 32], [119, 7321, 48], [37, 57, 4962]], 
* Val   accuracy / confusion: 97.77% / [[1054, 8, 7], [25, 899, 14], [2, 3, 628]]

------------------------------ Epoch 075 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.003254
- Iter 262 / 659, Loss: 0.033495
- Iter 393 / 659, Loss: 0.031911
- Iter 524 / 659, Loss: 0.023078
- Iter 655 / 659, Loss: 0.063490

* Train accuracy / confusion: 98.22% / [[8412, 103, 29], [112, 7326, 53], [37, 42, 4974]], 
* Val   accuracy / confusion: 98.18% / [[1051, 16, 2], [12, 923, 3], [10, 5, 618]]

------------------------------ Epoch 076 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.015203
- Iter 262 / 659, Loss: 0.023450
- Iter 393 / 659, Loss: 0.365898
- Iter 524 / 659, Loss: 0.068136
- Iter 655 / 659, Loss: 0.075616

* Train accuracy / confusion: 98.35% / [[8432, 79, 35], [98, 7343, 47], [40, 49, 4965]], 
* Val   accuracy / confusion: 97.16% / [[1040, 20, 9], [16, 909, 13], [6, 11, 616]]

------------------------------ Epoch 077 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.012037
- Iter 262 / 659, Loss: 0.007885
- Iter 393 / 659, Loss: 0.071707
- Iter 524 / 659, Loss: 0.005912
- Iter 655 / 659, Loss: 0.016646

* Train accuracy / confusion: 98.50% / [[8438, 72, 36], [99, 7351, 39], [34, 37, 4982]], 
* Val   accuracy / confusion: 97.50% / [[1059, 9, 1], [19, 918, 1], [26, 10, 597]]

------------------------------ Epoch 078 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.142272
- Iter 262 / 659, Loss: 0.005437
- Iter 393 / 659, Loss: 0.221278
- Iter 524 / 659, Loss: 0.005229
- Iter 655 / 659, Loss: 0.136044

* Train accuracy / confusion: 98.36% / [[8417, 80, 40], [99, 7354, 40], [42, 45, 4971]], 
* Val   accuracy / confusion: 97.95% / [[1048, 14, 7], [15, 913, 10], [5, 3, 625]]

------------------------------ Epoch 079 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.026063
- Iter 262 / 659, Loss: 0.002898
- Iter 393 / 659, Loss: 0.001426
- Iter 524 / 659, Loss: 0.044061
- Iter 655 / 659, Loss: 0.074778

* Train accuracy / confusion: 98.58% / [[8437, 78, 29], [84, 7368, 35], [36, 38, 4983]], 
* Val   accuracy / confusion: 97.31% / [[1057, 11, 1], [18, 916, 4], [31, 6, 596]]

------------------------------ Epoch 080 / 200, Learning rate: 3.45e-03 ------------------------------
- Iter 131 / 659, Loss: 0.000659
- Iter 262 / 659, Loss: 0.077646
- Iter 393 / 659, Loss: 0.018873
- Iter 524 / 659, Loss: 0.101831
- Iter 655 / 659, Loss: 0.018797

* Train accuracy / confusion: 98.54% / [[8428, 80, 35], [89, 7368, 35], [35, 34, 4984]], 
* Val   accuracy / confusion: 97.08% / [[1062, 5, 2], [30, 905, 3], [23, 14, 596]]

------------------------------ Epoch 081 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.004148
- Iter 262 / 659, Loss: 0.004218
- Iter 393 / 659, Loss: 0.003475
- Iter 524 / 659, Loss: 0.006098
- Iter 655 / 659, Loss: 0.002613

* Train accuracy / confusion: 99.26% / [[8499, 34, 10], [53, 7419, 18], [16, 25, 5014]], 
* Val   accuracy / confusion: 98.90% / [[1064, 2, 3], [11, 925, 2], [5, 6, 622]]

------------------------------ Epoch 082 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000974
- Iter 262 / 659, Loss: 0.010827
- Iter 393 / 659, Loss: 0.010126
- Iter 524 / 659, Loss: 0.015246
- Iter 655 / 659, Loss: 0.000435

* Train accuracy / confusion: 99.55% / [[8518, 19, 8], [30, 7442, 14], [12, 12, 5033]], 
* Val   accuracy / confusion: 98.60% / [[1057, 8, 4], [11, 925, 2], [5, 7, 621]]

------------------------------ Epoch 083 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.005960
- Iter 262 / 659, Loss: 0.000559
- Iter 393 / 659, Loss: 0.001694
- Iter 524 / 659, Loss: 0.001604
- Iter 655 / 659, Loss: 0.001472

* Train accuracy / confusion: 99.64% / [[8508, 18, 13], [24, 7461, 10], [3, 8, 5043]], 
* Val   accuracy / confusion: 98.64% / [[1064, 3, 2], [15, 921, 2], [7, 7, 619]]

------------------------------ Epoch 084 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000589
- Iter 262 / 659, Loss: 0.002938
- Iter 393 / 659, Loss: 0.000196
- Iter 524 / 659, Loss: 0.000842
- Iter 655 / 659, Loss: 0.057762

* Train accuracy / confusion: 99.62% / [[8519, 22, 6], [33, 7446, 8], [6, 6, 5042]], 
* Val   accuracy / confusion: 98.41% / [[1053, 13, 3], [9, 924, 5], [3, 9, 621]]

------------------------------ Epoch 085 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.004695
- Iter 262 / 659, Loss: 0.044011
- Iter 393 / 659, Loss: 0.000560
- Iter 524 / 659, Loss: 0.000758
- Iter 655 / 659, Loss: 0.001802

* Train accuracy / confusion: 99.72% / [[8525, 19, 2], [20, 7455, 11], [5, 3, 5048]], 
* Val   accuracy / confusion: 98.86% / [[1060, 6, 3], [8, 927, 3], [5, 5, 623]]

------------------------------ Epoch 086 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.001539
- Iter 262 / 659, Loss: 0.000142
- Iter 393 / 659, Loss: 0.042618
- Iter 524 / 659, Loss: 0.000131
- Iter 655 / 659, Loss: 0.004785

* Train accuracy / confusion: 99.69% / [[8515, 20, 5], [21, 7466, 8], [4, 7, 5042]], 
* Val   accuracy / confusion: 98.90% / [[1065, 1, 3], [12, 923, 3], [4, 6, 623]]

------------------------------ Epoch 087 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.001429
- Iter 262 / 659, Loss: 0.001240
- Iter 393 / 659, Loss: 0.001732
- Iter 524 / 659, Loss: 0.145669
- Iter 655 / 659, Loss: 0.000124

* Train accuracy / confusion: 99.74% / [[8522, 15, 4], [20, 7466, 5], [6, 5, 5045]], 
* Val   accuracy / confusion: 99.02% / [[1061, 5, 3], [6, 927, 5], [5, 2, 626]]

------------------------------ Epoch 088 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.001169
- Iter 262 / 659, Loss: 0.000029
- Iter 393 / 659, Loss: 0.010427
- Iter 524 / 659, Loss: 0.001478
- Iter 655 / 659, Loss: 0.000409

* Train accuracy / confusion: 99.72% / [[8521, 20, 3], [21, 7464, 6], [3, 6, 5044]], 
* Val   accuracy / confusion: 98.83% / [[1062, 5, 2], [11, 922, 5], [5, 3, 625]]

------------------------------ Epoch 089 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.001853
- Iter 262 / 659, Loss: 0.000091
- Iter 393 / 659, Loss: 0.007128
- Iter 524 / 659, Loss: 0.000820
- Iter 655 / 659, Loss: 0.000097

* Train accuracy / confusion: 99.72% / [[8521, 13, 8], [14, 7468, 8], [10, 6, 5040]], 
* Val   accuracy / confusion: 98.86% / [[1066, 1, 2], [10, 924, 4], [8, 5, 620]]

------------------------------ Epoch 090 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000834
- Iter 262 / 659, Loss: 0.006696
- Iter 393 / 659, Loss: 0.001784
- Iter 524 / 659, Loss: 0.000593
- Iter 655 / 659, Loss: 0.000363

* Train accuracy / confusion: 99.70% / [[8520, 17, 4], [15, 7466, 11], [5, 11, 5039]], 
* Val   accuracy / confusion: 98.98% / [[1063, 4, 2], [12, 924, 2], [6, 1, 626]]

------------------------------ Epoch 091 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.021370
- Iter 262 / 659, Loss: 0.001327
- Iter 393 / 659, Loss: 0.067825
- Iter 524 / 659, Loss: 0.000073
- Iter 655 / 659, Loss: 0.000399

* Train accuracy / confusion: 99.76% / [[8527, 17, 3], [14, 7463, 7], [5, 5, 5047]], 
* Val   accuracy / confusion: 99.02% / [[1066, 1, 2], [14, 919, 5], [1, 3, 629]]

------------------------------ Epoch 092 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000254
- Iter 262 / 659, Loss: 0.006233
- Iter 393 / 659, Loss: 0.009916
- Iter 524 / 659, Loss: 0.000009
- Iter 655 / 659, Loss: 0.001923

* Train accuracy / confusion: 99.77% / [[8522, 14, 5], [20, 7469, 2], [2, 5, 5049]], 
* Val   accuracy / confusion: 98.75% / [[1060, 7, 2], [11, 923, 4], [5, 4, 624]]

------------------------------ Epoch 093 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000300
- Iter 262 / 659, Loss: 0.000118
- Iter 393 / 659, Loss: 0.000334
- Iter 524 / 659, Loss: 0.001350
- Iter 655 / 659, Loss: 0.147373

* Train accuracy / confusion: 99.77% / [[8522, 15, 5], [16, 7475, 3], [2, 7, 5043]], 
* Val   accuracy / confusion: 98.90% / [[1056, 10, 3], [6, 930, 2], [3, 5, 625]]

------------------------------ Epoch 094 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.001475
- Iter 262 / 659, Loss: 0.000143
- Iter 393 / 659, Loss: 0.000729
- Iter 524 / 659, Loss: 0.000066
- Iter 655 / 659, Loss: 0.002902

* Train accuracy / confusion: 99.82% / [[8532, 10, 4], [8, 7472, 5], [6, 6, 5045]], 
* Val   accuracy / confusion: 99.09% / [[1065, 2, 2], [11, 923, 4], [2, 3, 628]]

------------------------------ Epoch 095 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000061
- Iter 262 / 659, Loss: 0.001018
- Iter 393 / 659, Loss: 0.002031
- Iter 524 / 659, Loss: 0.001422
- Iter 655 / 659, Loss: 0.000150

* Train accuracy / confusion: 99.82% / [[8529, 12, 2], [15, 7472, 6], [1, 3, 5048]], 
* Val   accuracy / confusion: 98.94% / [[1061, 5, 3], [9, 927, 2], [4, 5, 624]]

------------------------------ Epoch 096 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000076
- Iter 262 / 659, Loss: 0.000379
- Iter 393 / 659, Loss: 0.000472
- Iter 524 / 659, Loss: 0.001025
- Iter 655 / 659, Loss: 0.070004

* Train accuracy / confusion: 99.77% / [[8528, 12, 6], [11, 7471, 6], [7, 6, 5041]], 
* Val   accuracy / confusion: 98.86% / [[1062, 3, 4], [10, 924, 4], [6, 3, 624]]

------------------------------ Epoch 097 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000156
- Iter 262 / 659, Loss: 0.003856
- Iter 393 / 659, Loss: 0.009639
- Iter 524 / 659, Loss: 0.000794
- Iter 655 / 659, Loss: 0.000770

* Train accuracy / confusion: 99.86% / [[8528, 10, 3], [9, 7481, 3], [3, 2, 5049]], 
* Val   accuracy / confusion: 99.09% / [[1062, 5, 2], [8, 927, 3], [4, 2, 627]]

------------------------------ Epoch 098 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000038
- Iter 262 / 659, Loss: 0.008041
- Iter 393 / 659, Loss: 0.000030
- Iter 524 / 659, Loss: 0.000677
- Iter 655 / 659, Loss: 0.004767

* Train accuracy / confusion: 99.82% / [[8527, 13, 3], [11, 7473, 4], [5, 1, 5051]], 
* Val   accuracy / confusion: 98.86% / [[1061, 6, 2], [9, 928, 1], [6, 6, 621]]

------------------------------ Epoch 099 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000416
- Iter 262 / 659, Loss: 0.004902
- Iter 393 / 659, Loss: 0.002884
- Iter 524 / 659, Loss: 0.000279
- Iter 655 / 659, Loss: 0.000082

* Train accuracy / confusion: 99.77% / [[8525, 12, 5], [16, 7468, 5], [5, 5, 5047]], 
* Val   accuracy / confusion: 98.94% / [[1066, 1, 2], [10, 924, 4], [5, 6, 622]]

------------------------------ Epoch 100 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000020
- Iter 262 / 659, Loss: 0.000539
- Iter 393 / 659, Loss: 0.001522
- Iter 524 / 659, Loss: 0.000038
- Iter 655 / 659, Loss: 0.000965

* Train accuracy / confusion: 99.80% / [[8525, 9, 7], [12, 7472, 6], [3, 6, 5048]], 
* Val   accuracy / confusion: 99.02% / [[1058, 9, 2], [9, 928, 1], [1, 4, 628]]

------------------------------ Epoch 101 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000074
- Iter 262 / 659, Loss: 0.000287
- Iter 393 / 659, Loss: 0.000042
- Iter 524 / 659, Loss: 0.000615
- Iter 655 / 659, Loss: 0.000565

* Train accuracy / confusion: 99.83% / [[8536, 12, 2], [13, 7470, 5], [3, 1, 5046]], 
* Val   accuracy / confusion: 99.05% / [[1058, 8, 3], [7, 929, 2], [0, 5, 628]]

------------------------------ Epoch 102 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.004024
- Iter 262 / 659, Loss: 0.000296
- Iter 393 / 659, Loss: 0.001951
- Iter 524 / 659, Loss: 0.000981
- Iter 655 / 659, Loss: 0.018192

* Train accuracy / confusion: 99.90% / [[8535, 5, 4], [8, 7481, 3], [1, 1, 5050]], 
* Val   accuracy / confusion: 99.09% / [[1066, 2, 1], [10, 926, 2], [7, 2, 624]]

------------------------------ Epoch 103 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000020
- Iter 262 / 659, Loss: 0.000385
- Iter 393 / 659, Loss: 0.000295
- Iter 524 / 659, Loss: 0.003651
- Iter 655 / 659, Loss: 0.000572

* Train accuracy / confusion: 99.86% / [[8530, 11, 1], [10, 7478, 3], [2, 2, 5051]], 
* Val   accuracy / confusion: 99.02% / [[1065, 1, 3], [11, 924, 3], [5, 3, 625]]

------------------------------ Epoch 104 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000668
- Iter 262 / 659, Loss: 0.000210
- Iter 393 / 659, Loss: 0.001057
- Iter 524 / 659, Loss: 0.000932
- Iter 655 / 659, Loss: 0.000009

* Train accuracy / confusion: 99.84% / [[8529, 6, 7], [11, 7473, 3], [3, 4, 5052]], 
* Val   accuracy / confusion: 98.83% / [[1060, 7, 2], [10, 925, 3], [5, 4, 624]]

------------------------------ Epoch 105 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.002316
- Iter 262 / 659, Loss: 0.001253
- Iter 393 / 659, Loss: 0.001606
- Iter 524 / 659, Loss: 0.000314
- Iter 655 / 659, Loss: 0.000096

* Train accuracy / confusion: 99.84% / [[8533, 8, 3], [10, 7476, 3], [3, 6, 5046]], 
* Val   accuracy / confusion: 99.09% / [[1062, 5, 2], [9, 928, 1], [4, 3, 626]]

------------------------------ Epoch 106 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000063
- Iter 262 / 659, Loss: 0.001232
- Iter 393 / 659, Loss: 0.107322
- Iter 524 / 659, Loss: 0.001022
- Iter 655 / 659, Loss: 0.000365

* Train accuracy / confusion: 99.84% / [[8535, 8, 2], [14, 7469, 5], [1, 3, 5051]], 
* Val   accuracy / confusion: 99.02% / [[1062, 6, 1], [10, 926, 2], [4, 3, 626]]

------------------------------ Epoch 107 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000941
- Iter 262 / 659, Loss: 0.000289
- Iter 393 / 659, Loss: 0.000013
- Iter 524 / 659, Loss: 0.000197
- Iter 655 / 659, Loss: 0.000028

* Train accuracy / confusion: 99.86% / [[8531, 6, 3], [8, 7480, 3], [4, 5, 5048]], 
* Val   accuracy / confusion: 98.79% / [[1059, 8, 2], [11, 921, 6], [1, 4, 628]]

------------------------------ Epoch 108 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000725
- Iter 262 / 659, Loss: 0.006260
- Iter 393 / 659, Loss: 0.000066
- Iter 524 / 659, Loss: 0.000208
- Iter 655 / 659, Loss: 0.000202

* Train accuracy / confusion: 99.85% / [[8532, 8, 4], [9, 7477, 5], [1, 4, 5048]], 
* Val   accuracy / confusion: 99.20% / [[1064, 2, 3], [9, 929, 0], [5, 2, 626]]

------------------------------ Epoch 109 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000012
- Iter 262 / 659, Loss: 0.000103
- Iter 393 / 659, Loss: 0.006343
- Iter 524 / 659, Loss: 0.000110
- Iter 655 / 659, Loss: 0.000319

* Train accuracy / confusion: 99.89% / [[8536, 7, 1], [7, 7476, 3], [2, 3, 5053]], 
* Val   accuracy / confusion: 99.05% / [[1062, 5, 2], [9, 926, 3], [3, 3, 627]]

------------------------------ Epoch 110 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000008
- Iter 262 / 659, Loss: 0.000227
- Iter 393 / 659, Loss: 0.000017
- Iter 524 / 659, Loss: 0.002231
- Iter 655 / 659, Loss: 0.000009

* Train accuracy / confusion: 99.89% / [[8535, 4, 4], [7, 7480, 2], [3, 4, 5049]], 
* Val   accuracy / confusion: 99.13% / [[1066, 3, 0], [12, 924, 2], [5, 1, 627]]

------------------------------ Epoch 111 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000162
- Iter 262 / 659, Loss: 0.000800
- Iter 393 / 659, Loss: 0.000682
- Iter 524 / 659, Loss: 0.011214
- Iter 655 / 659, Loss: 0.000477

* Train accuracy / confusion: 99.91% / [[8538, 5, 1], [7, 7483, 2], [4, 1, 5047]], 
* Val   accuracy / confusion: 98.90% / [[1060, 5, 4], [10, 923, 5], [2, 3, 628]]

------------------------------ Epoch 112 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000888
- Iter 262 / 659, Loss: 0.000011
- Iter 393 / 659, Loss: 0.002176
- Iter 524 / 659, Loss: 0.000044
- Iter 655 / 659, Loss: 0.000434

* Train accuracy / confusion: 99.88% / [[8536, 8, 0], [9, 7477, 4], [2, 2, 5050]], 
* Val   accuracy / confusion: 98.94% / [[1061, 5, 3], [11, 925, 2], [3, 4, 626]]

------------------------------ Epoch 113 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000556
- Iter 262 / 659, Loss: 0.000038
- Iter 393 / 659, Loss: 0.001951
- Iter 524 / 659, Loss: 0.000318
- Iter 655 / 659, Loss: 0.043723

* Train accuracy / confusion: 99.84% / [[8529, 15, 0], [11, 7477, 2], [4, 2, 5048]], 
* Val   accuracy / confusion: 99.17% / [[1063, 3, 3], [7, 926, 5], [1, 3, 629]]

------------------------------ Epoch 114 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000174
- Iter 262 / 659, Loss: 0.000215
- Iter 393 / 659, Loss: 0.000053
- Iter 524 / 659, Loss: 0.000070
- Iter 655 / 659, Loss: 0.000303

* Train accuracy / confusion: 99.87% / [[8533, 10, 1], [9, 7475, 3], [1, 4, 5052]], 
* Val   accuracy / confusion: 98.90% / [[1063, 3, 3], [12, 922, 4], [5, 2, 626]]

------------------------------ Epoch 115 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.001384
- Iter 262 / 659, Loss: 0.000322
- Iter 393 / 659, Loss: 0.000031
- Iter 524 / 659, Loss: 0.000322
- Iter 655 / 659, Loss: 0.000298

* Train accuracy / confusion: 99.82% / [[8524, 10, 8], [9, 7477, 3], [5, 4, 5048]], 
* Val   accuracy / confusion: 99.02% / [[1063, 5, 1], [9, 925, 4], [4, 3, 626]]

------------------------------ Epoch 116 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.002997
- Iter 262 / 659, Loss: 0.001427
- Iter 393 / 659, Loss: 0.000076
- Iter 524 / 659, Loss: 0.000115
- Iter 655 / 659, Loss: 0.000785

* Train accuracy / confusion: 99.92% / [[8534, 5, 1], [8, 7486, 2], [1, 0, 5051]], 
* Val   accuracy / confusion: 99.13% / [[1063, 2, 4], [8, 929, 1], [3, 5, 625]]

------------------------------ Epoch 117 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000628
- Iter 262 / 659, Loss: 0.000153
- Iter 393 / 659, Loss: 0.000068
- Iter 524 / 659, Loss: 0.000584
- Iter 655 / 659, Loss: 0.000150

* Train accuracy / confusion: 99.91% / [[8535, 4, 1], [5, 7486, 2], [4, 3, 5048]], 
* Val   accuracy / confusion: 98.90% / [[1064, 2, 3], [15, 921, 2], [4, 3, 626]]

------------------------------ Epoch 118 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000702
- Iter 262 / 659, Loss: 0.000209
- Iter 393 / 659, Loss: 0.000491
- Iter 524 / 659, Loss: 0.000218
- Iter 655 / 659, Loss: 0.000004

* Train accuracy / confusion: 99.91% / [[8540, 4, 1], [7, 7476, 3], [2, 2, 5053]], 
* Val   accuracy / confusion: 98.83% / [[1059, 7, 3], [9, 927, 2], [5, 5, 623]]

------------------------------ Epoch 119 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000036
- Iter 262 / 659, Loss: 0.000007
- Iter 393 / 659, Loss: 0.000005
- Iter 524 / 659, Loss: 0.000189
- Iter 655 / 659, Loss: 0.000023

* Train accuracy / confusion: 99.88% / [[8538, 8, 1], [11, 7473, 2], [1, 2, 5052]], 
* Val   accuracy / confusion: 99.09% / [[1061, 5, 3], [6, 928, 4], [2, 4, 627]]

------------------------------ Epoch 120 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000264
- Iter 262 / 659, Loss: 0.000343
- Iter 393 / 659, Loss: 0.007965
- Iter 524 / 659, Loss: 0.002483
- Iter 655 / 659, Loss: 0.000005

* Train accuracy / confusion: 99.92% / [[8536, 4, 1], [5, 7484, 2], [0, 5, 5051]], 
* Val   accuracy / confusion: 99.02% / [[1063, 5, 1], [9, 927, 2], [2, 7, 624]]

------------------------------ Epoch 121 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000068
- Iter 262 / 659, Loss: 0.000135
- Iter 393 / 659, Loss: 0.001507
- Iter 524 / 659, Loss: 0.000015
- Iter 655 / 659, Loss: 0.000014

* Train accuracy / confusion: 99.89% / [[8533, 8, 1], [10, 7477, 2], [1, 2, 5054]], 
* Val   accuracy / confusion: 99.13% / [[1063, 5, 1], [9, 927, 2], [4, 2, 627]]

------------------------------ Epoch 122 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000007
- Iter 262 / 659, Loss: 0.000003
- Iter 393 / 659, Loss: 0.000025
- Iter 524 / 659, Loss: 0.000247
- Iter 655 / 659, Loss: 0.000061

* Train accuracy / confusion: 99.89% / [[8536, 4, 2], [9, 7474, 4], [2, 3, 5054]], 
* Val   accuracy / confusion: 99.17% / [[1064, 3, 2], [8, 927, 3], [2, 4, 627]]

------------------------------ Epoch 123 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000030
- Iter 262 / 659, Loss: 0.000014
- Iter 393 / 659, Loss: 0.000080
- Iter 524 / 659, Loss: 0.000022
- Iter 655 / 659, Loss: 0.000033

* Train accuracy / confusion: 99.90% / [[8536, 5, 2], [6, 7480, 3], [2, 3, 5051]], 
* Val   accuracy / confusion: 99.09% / [[1062, 5, 2], [7, 929, 2], [5, 3, 625]]

------------------------------ Epoch 124 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000393
- Iter 262 / 659, Loss: 0.000160
- Iter 393 / 659, Loss: 0.000971
- Iter 524 / 659, Loss: 0.000066
- Iter 655 / 659, Loss: 0.000003

* Train accuracy / confusion: 99.89% / [[8536, 4, 3], [6, 7479, 4], [2, 5, 5049]], 
* Val   accuracy / confusion: 99.28% / [[1063, 6, 0], [7, 930, 1], [1, 4, 628]]

------------------------------ Epoch 125 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.002258
- Iter 262 / 659, Loss: 0.000004
- Iter 393 / 659, Loss: 0.000069
- Iter 524 / 659, Loss: 0.000019
- Iter 655 / 659, Loss: 0.000279

* Train accuracy / confusion: 99.89% / [[8535, 5, 3], [9, 7480, 1], [3, 2, 5050]], 
* Val   accuracy / confusion: 99.09% / [[1061, 7, 1], [5, 932, 1], [6, 4, 623]]

------------------------------ Epoch 126 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000332
- Iter 262 / 659, Loss: 0.000056
- Iter 393 / 659, Loss: 0.000234
- Iter 524 / 659, Loss: 0.000210
- Iter 655 / 659, Loss: 0.000102

* Train accuracy / confusion: 99.89% / [[8532, 7, 1], [8, 7481, 3], [2, 3, 5051]], 
* Val   accuracy / confusion: 98.86% / [[1063, 4, 2], [11, 922, 5], [7, 1, 625]]

------------------------------ Epoch 127 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000047
- Iter 262 / 659, Loss: 0.000029
- Iter 393 / 659, Loss: 0.003657
- Iter 524 / 659, Loss: 0.000414
- Iter 655 / 659, Loss: 0.000847

* Train accuracy / confusion: 99.94% / [[8537, 4, 0], [6, 7484, 1], [1, 0, 5055]], 
* Val   accuracy / confusion: 99.05% / [[1063, 4, 2], [8, 927, 3], [1, 7, 625]]

------------------------------ Epoch 128 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000221
- Iter 262 / 659, Loss: 0.000941
- Iter 393 / 659, Loss: 0.000133
- Iter 524 / 659, Loss: 0.000004
- Iter 655 / 659, Loss: 0.000026

* Train accuracy / confusion: 99.91% / [[8537, 5, 2], [6, 7484, 2], [4, 0, 5048]], 
* Val   accuracy / confusion: 98.94% / [[1056, 8, 5], [8, 926, 4], [2, 1, 630]]

------------------------------ Epoch 129 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000244
- Iter 262 / 659, Loss: 0.002675
- Iter 393 / 659, Loss: 0.000073
- Iter 524 / 659, Loss: 0.000130
- Iter 655 / 659, Loss: 0.000037

* Train accuracy / confusion: 99.89% / [[8537, 6, 3], [4, 7480, 5], [2, 3, 5048]], 
* Val   accuracy / confusion: 99.05% / [[1062, 4, 3], [8, 928, 2], [4, 4, 625]]

------------------------------ Epoch 130 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000084
- Iter 262 / 659, Loss: 0.000070
- Iter 393 / 659, Loss: 0.000022
- Iter 524 / 659, Loss: 0.009928
- Iter 655 / 659, Loss: 0.000644

* Train accuracy / confusion: 99.95% / [[8540, 2, 1], [2, 7485, 2], [1, 3, 5052]], 
* Val   accuracy / confusion: 99.09% / [[1062, 5, 2], [9, 927, 2], [3, 3, 627]]

------------------------------ Epoch 131 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000097
- Iter 262 / 659, Loss: 0.000009
- Iter 393 / 659, Loss: 0.000153
- Iter 524 / 659, Loss: 0.114918
- Iter 655 / 659, Loss: 0.000010

* Train accuracy / confusion: 99.88% / [[8533, 10, 1], [8, 7482, 2], [2, 2, 5048]], 
* Val   accuracy / confusion: 99.05% / [[1063, 4, 2], [6, 929, 3], [4, 6, 623]]

------------------------------ Epoch 132 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.002274
- Iter 262 / 659, Loss: 0.000078
- Iter 393 / 659, Loss: 0.000082
- Iter 524 / 659, Loss: 0.000009
- Iter 655 / 659, Loss: 0.000508

* Train accuracy / confusion: 99.92% / [[8539, 4, 1], [8, 7481, 1], [1, 1, 5052]], 
* Val   accuracy / confusion: 99.09% / [[1068, 0, 1], [12, 923, 3], [7, 1, 625]]

------------------------------ Epoch 133 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000037
- Iter 262 / 659, Loss: 0.000028
- Iter 393 / 659, Loss: 0.000082
- Iter 524 / 659, Loss: 0.000016
- Iter 655 / 659, Loss: 0.000974

* Train accuracy / confusion: 99.87% / [[8530, 7, 2], [8, 7481, 4], [3, 3, 5050]], 
* Val   accuracy / confusion: 99.02% / [[1062, 4, 3], [8, 927, 3], [3, 5, 625]]

------------------------------ Epoch 134 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000304
- Iter 262 / 659, Loss: 0.000336
- Iter 393 / 659, Loss: 0.000649
- Iter 524 / 659, Loss: 0.000027
- Iter 655 / 659, Loss: 0.000708

* Train accuracy / confusion: 99.95% / [[8540, 5, 1], [1, 7484, 3], [1, 0, 5053]], 
* Val   accuracy / confusion: 99.09% / [[1063, 3, 3], [9, 929, 0], [7, 2, 624]]

------------------------------ Epoch 135 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000797
- Iter 262 / 659, Loss: 0.000398
- Iter 393 / 659, Loss: 0.000016
- Iter 524 / 659, Loss: 0.000008
- Iter 655 / 659, Loss: 0.000565

* Train accuracy / confusion: 99.90% / [[8537, 8, 2], [8, 7478, 1], [1, 2, 5051]], 
* Val   accuracy / confusion: 99.05% / [[1065, 2, 2], [8, 927, 3], [7, 3, 623]]

------------------------------ Epoch 136 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000011
- Iter 262 / 659, Loss: 0.000010
- Iter 393 / 659, Loss: 0.000081
- Iter 524 / 659, Loss: 0.000016
- Iter 655 / 659, Loss: 0.000255

* Train accuracy / confusion: 99.89% / [[8534, 7, 2], [9, 7480, 2], [2, 2, 5050]], 
* Val   accuracy / confusion: 98.86% / [[1062, 4, 3], [12, 925, 1], [5, 5, 623]]

------------------------------ Epoch 137 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000001
- Iter 262 / 659, Loss: 0.000069
- Iter 393 / 659, Loss: 0.000068
- Iter 524 / 659, Loss: 0.000045
- Iter 655 / 659, Loss: 0.000001

* Train accuracy / confusion: 99.93% / [[8534, 5, 1], [4, 7487, 2], [2, 1, 5052]], 
* Val   accuracy / confusion: 99.09% / [[1060, 6, 3], [6, 930, 2], [6, 1, 626]]

------------------------------ Epoch 138 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000107
- Iter 262 / 659, Loss: 0.000064
- Iter 393 / 659, Loss: 0.000120
- Iter 524 / 659, Loss: 0.000051
- Iter 655 / 659, Loss: 0.000869

* Train accuracy / confusion: 99.91% / [[8535, 8, 2], [8, 7479, 1], [0, 0, 5055]], 
* Val   accuracy / confusion: 99.32% / [[1060, 7, 2], [5, 931, 2], [1, 1, 631]]

------------------------------ Epoch 139 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000156
- Iter 262 / 659, Loss: 0.000031
- Iter 393 / 659, Loss: 0.000009
- Iter 524 / 659, Loss: 0.000172
- Iter 655 / 659, Loss: 0.000030

* Train accuracy / confusion: 99.88% / [[8534, 5, 6], [8, 7483, 2], [3, 2, 5045]], 
* Val   accuracy / confusion: 98.83% / [[1057, 8, 4], [7, 927, 4], [4, 4, 625]]

------------------------------ Epoch 140 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000019
- Iter 262 / 659, Loss: 0.000277
- Iter 393 / 659, Loss: 0.000484
- Iter 524 / 659, Loss: 0.000029
- Iter 655 / 659, Loss: 0.000314

* Train accuracy / confusion: 99.87% / [[8533, 7, 2], [6, 7480, 5], [1, 6, 5048]], 
* Val   accuracy / confusion: 99.02% / [[1061, 5, 3], [7, 928, 3], [3, 5, 625]]

------------------------------ Epoch 141 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000118
- Iter 262 / 659, Loss: 0.000569
- Iter 393 / 659, Loss: 0.000108
- Iter 524 / 659, Loss: 0.000006
- Iter 655 / 659, Loss: 0.004458

* Train accuracy / confusion: 99.87% / [[8532, 9, 2], [6, 7478, 5], [4, 1, 5051]], 
* Val   accuracy / confusion: 99.05% / [[1060, 7, 2], [6, 932, 0], [6, 4, 623]]

------------------------------ Epoch 142 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000019
- Iter 262 / 659, Loss: 0.000159
- Iter 393 / 659, Loss: 0.000774
- Iter 524 / 659, Loss: 0.000044
- Iter 655 / 659, Loss: 0.000032

* Train accuracy / confusion: 99.92% / [[8538, 6, 0], [7, 7483, 1], [3, 0, 5050]], 
* Val   accuracy / confusion: 99.13% / [[1058, 8, 3], [5, 930, 3], [0, 4, 629]]

------------------------------ Epoch 143 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000619
- Iter 262 / 659, Loss: 0.000030
- Iter 393 / 659, Loss: 0.075659
- Iter 524 / 659, Loss: 0.000055
- Iter 655 / 659, Loss: 0.000028

* Train accuracy / confusion: 99.88% / [[8536, 4, 4], [4, 7485, 5], [4, 4, 5042]], 
* Val   accuracy / confusion: 99.05% / [[1066, 0, 3], [11, 926, 1], [7, 3, 623]]

------------------------------ Epoch 144 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000012
- Iter 262 / 659, Loss: 0.001474
- Iter 393 / 659, Loss: 0.000074
- Iter 524 / 659, Loss: 0.000054
- Iter 655 / 659, Loss: 0.000119

* Train accuracy / confusion: 99.91% / [[8540, 7, 1], [4, 7485, 2], [1, 3, 5045]], 
* Val   accuracy / confusion: 99.36% / [[1068, 0, 1], [9, 927, 2], [4, 1, 628]]

------------------------------ Epoch 145 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000039
- Iter 262 / 659, Loss: 0.000039
- Iter 393 / 659, Loss: 0.000039
- Iter 524 / 659, Loss: 0.000353
- Iter 655 / 659, Loss: 0.000076

* Train accuracy / confusion: 99.94% / [[8542, 3, 0], [3, 7482, 4], [1, 1, 5052]], 
* Val   accuracy / confusion: 98.90% / [[1062, 4, 3], [10, 927, 1], [2, 9, 622]]

------------------------------ Epoch 146 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.002073
- Iter 262 / 659, Loss: 0.000008
- Iter 393 / 659, Loss: 0.000013
- Iter 524 / 659, Loss: 0.000177
- Iter 655 / 659, Loss: 0.000013

* Train accuracy / confusion: 99.89% / [[8534, 8, 2], [4, 7479, 3], [6, 1, 5051]], 
* Val   accuracy / confusion: 99.02% / [[1063, 3, 3], [10, 926, 2], [2, 6, 625]]

------------------------------ Epoch 147 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000131
- Iter 262 / 659, Loss: 0.000003
- Iter 393 / 659, Loss: 0.000060
- Iter 524 / 659, Loss: 0.000017
- Iter 655 / 659, Loss: 0.000090

* Train accuracy / confusion: 99.96% / [[8531, 3, 1], [2, 7492, 2], [0, 1, 5056]], 
* Val   accuracy / confusion: 98.98% / [[1065, 2, 2], [11, 923, 4], [3, 5, 625]]

------------------------------ Epoch 148 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000065
- Iter 262 / 659, Loss: 0.000004
- Iter 393 / 659, Loss: 0.000297
- Iter 524 / 659, Loss: 0.000006
- Iter 655 / 659, Loss: 0.000944

* Train accuracy / confusion: 99.95% / [[8538, 3, 1], [4, 7489, 0], [2, 0, 5051]], 
* Val   accuracy / confusion: 99.05% / [[1062, 3, 4], [9, 927, 2], [3, 4, 626]]

------------------------------ Epoch 149 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000010
- Iter 262 / 659, Loss: 0.000608
- Iter 393 / 659, Loss: 0.000057
- Iter 524 / 659, Loss: 0.000004
- Iter 655 / 659, Loss: 0.000029

* Train accuracy / confusion: 99.91% / [[8531, 7, 2], [6, 7482, 2], [3, 0, 5055]], 
* Val   accuracy / confusion: 98.94% / [[1059, 4, 6], [10, 927, 1], [3, 4, 626]]

------------------------------ Epoch 150 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.002857
- Iter 262 / 659, Loss: 0.000221
- Iter 393 / 659, Loss: 0.000109
- Iter 524 / 659, Loss: 0.000008
- Iter 655 / 659, Loss: 0.000102

* Train accuracy / confusion: 99.87% / [[8531, 9, 0], [9, 7478, 6], [1, 2, 5052]], 
* Val   accuracy / confusion: 99.17% / [[1065, 1, 3], [9, 927, 2], [2, 5, 626]]

------------------------------ Epoch 151 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.001583
- Iter 262 / 659, Loss: 0.000015
- Iter 393 / 659, Loss: 0.000053
- Iter 524 / 659, Loss: 0.000010
- Iter 655 / 659, Loss: 0.000019

* Train accuracy / confusion: 99.94% / [[8534, 4, 2], [4, 7489, 0], [1, 2, 5052]], 
* Val   accuracy / confusion: 99.20% / [[1064, 5, 0], [8, 927, 3], [3, 2, 628]]

------------------------------ Epoch 152 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.063944
- Iter 262 / 659, Loss: 0.000019
- Iter 393 / 659, Loss: 0.000184
- Iter 524 / 659, Loss: 0.000031
- Iter 655 / 659, Loss: 0.000069

* Train accuracy / confusion: 99.93% / [[8535, 4, 2], [5, 7487, 0], [1, 2, 5052]], 
* Val   accuracy / confusion: 99.13% / [[1065, 1, 3], [9, 926, 3], [5, 2, 626]]

------------------------------ Epoch 153 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.002650
- Iter 262 / 659, Loss: 0.000003
- Iter 393 / 659, Loss: 0.000059
- Iter 524 / 659, Loss: 0.000018
- Iter 655 / 659, Loss: 0.000258

* Train accuracy / confusion: 99.89% / [[8535, 5, 2], [4, 7482, 6], [4, 3, 5047]], 
* Val   accuracy / confusion: 99.02% / [[1063, 3, 3], [9, 926, 3], [3, 5, 625]]

------------------------------ Epoch 154 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000014
- Iter 262 / 659, Loss: 0.000044
- Iter 393 / 659, Loss: 0.000045
- Iter 524 / 659, Loss: 0.000626
- Iter 655 / 659, Loss: 0.000482

* Train accuracy / confusion: 99.89% / [[8537, 5, 3], [7, 7480, 1], [5, 2, 5048]], 
* Val   accuracy / confusion: 98.86% / [[1064, 3, 2], [13, 921, 4], [6, 2, 625]]

------------------------------ Epoch 155 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000050
- Iter 262 / 659, Loss: 0.000029
- Iter 393 / 659, Loss: 0.000138
- Iter 524 / 659, Loss: 0.000020
- Iter 655 / 659, Loss: 0.035062

* Train accuracy / confusion: 99.93% / [[8537, 5, 1], [6, 7481, 2], [0, 1, 5055]], 
* Val   accuracy / confusion: 98.83% / [[1061, 6, 2], [12, 923, 3], [4, 4, 625]]

------------------------------ Epoch 156 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000022
- Iter 262 / 659, Loss: 0.000187
- Iter 393 / 659, Loss: 0.000025
- Iter 524 / 659, Loss: 0.000221
- Iter 655 / 659, Loss: 0.000040

* Train accuracy / confusion: 99.95% / [[8537, 3, 0], [5, 7490, 1], [0, 2, 5050]], 
* Val   accuracy / confusion: 98.86% / [[1061, 6, 2], [10, 926, 2], [5, 5, 623]]

------------------------------ Epoch 157 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000075
- Iter 262 / 659, Loss: 0.000390
- Iter 393 / 659, Loss: 0.000017
- Iter 524 / 659, Loss: 0.000030
- Iter 655 / 659, Loss: 0.000064

* Train accuracy / confusion: 99.93% / [[8537, 1, 3], [5, 7485, 2], [3, 0, 5052]], 
* Val   accuracy / confusion: 99.28% / [[1066, 1, 2], [10, 927, 1], [2, 3, 628]]

------------------------------ Epoch 158 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000013
- Iter 262 / 659, Loss: 0.000252
- Iter 393 / 659, Loss: 0.000010
- Iter 524 / 659, Loss: 0.000003
- Iter 655 / 659, Loss: 0.000009

* Train accuracy / confusion: 99.91% / [[8538, 3, 2], [4, 7483, 4], [1, 5, 5048]], 
* Val   accuracy / confusion: 98.98% / [[1065, 3, 1], [15, 923, 0], [4, 4, 625]]

------------------------------ Epoch 159 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.003561
- Iter 262 / 659, Loss: 0.000010
- Iter 393 / 659, Loss: 0.000175
- Iter 524 / 659, Loss: 0.000303
- Iter 655 / 659, Loss: 0.000002

* Train accuracy / confusion: 99.93% / [[8538, 0, 1], [5, 7485, 4], [1, 3, 5051]], 
* Val   accuracy / confusion: 99.20% / [[1060, 7, 2], [4, 932, 2], [4, 2, 627]]

------------------------------ Epoch 160 / 200, Learning rate: 3.45e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000854
- Iter 262 / 659, Loss: 0.000242
- Iter 393 / 659, Loss: 0.000012
- Iter 524 / 659, Loss: 0.000011
- Iter 655 / 659, Loss: 0.000066

* Train accuracy / confusion: 99.90% / [[8532, 4, 5], [5, 7484, 2], [0, 5, 5051]], 
* Val   accuracy / confusion: 98.94% / [[1065, 2, 2], [12, 923, 3], [8, 1, 624]]

------------------------------ Epoch 161 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000040
- Iter 262 / 659, Loss: 0.000345
- Iter 393 / 659, Loss: 0.007210
- Iter 524 / 659, Loss: 0.000502
- Iter 655 / 659, Loss: 0.000006

* Train accuracy / confusion: 99.92% / [[8543, 3, 0], [8, 7476, 3], [2, 1, 5052]], 
* Val   accuracy / confusion: 99.09% / [[1064, 2, 3], [9, 926, 3], [4, 3, 626]]

------------------------------ Epoch 162 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000283
- Iter 262 / 659, Loss: 0.000003
- Iter 393 / 659, Loss: 0.000063
- Iter 524 / 659, Loss: 0.000067
- Iter 655 / 659, Loss: 0.000116

* Train accuracy / confusion: 99.93% / [[8540, 2, 2], [2, 7487, 1], [4, 3, 5047]], 
* Val   accuracy / confusion: 98.98% / [[1063, 5, 1], [9, 924, 5], [3, 4, 626]]

------------------------------ Epoch 163 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000016
- Iter 262 / 659, Loss: 0.000068
- Iter 393 / 659, Loss: 0.000001
- Iter 524 / 659, Loss: 0.000060
- Iter 655 / 659, Loss: 0.000042

* Train accuracy / confusion: 99.94% / [[8541, 4, 0], [3, 7482, 3], [1, 2, 5052]], 
* Val   accuracy / confusion: 99.13% / [[1067, 1, 1], [11, 925, 2], [4, 4, 625]]

------------------------------ Epoch 164 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000422
- Iter 262 / 659, Loss: 0.000005
- Iter 393 / 659, Loss: 0.000011
- Iter 524 / 659, Loss: 0.000019
- Iter 655 / 659, Loss: 0.000012

* Train accuracy / confusion: 99.96% / [[8541, 2, 0], [4, 7483, 2], [1, 0, 5055]], 
* Val   accuracy / confusion: 99.05% / [[1066, 0, 3], [10, 924, 4], [6, 2, 625]]

------------------------------ Epoch 165 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000087
- Iter 262 / 659, Loss: 0.000109
- Iter 393 / 659, Loss: 0.000016
- Iter 524 / 659, Loss: 0.001243
- Iter 655 / 659, Loss: 0.002326

* Train accuracy / confusion: 99.94% / [[8533, 4, 1], [3, 7493, 2], [2, 1, 5049]], 
* Val   accuracy / confusion: 99.02% / [[1062, 5, 2], [10, 926, 2], [4, 3, 626]]

------------------------------ Epoch 166 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.006866
- Iter 262 / 659, Loss: 0.000002
- Iter 393 / 659, Loss: 0.000417
- Iter 524 / 659, Loss: 0.000047
- Iter 655 / 659, Loss: 0.000059

* Train accuracy / confusion: 99.95% / [[8536, 6, 0], [3, 7489, 1], [0, 1, 5052]], 
* Val   accuracy / confusion: 99.20% / [[1064, 5, 0], [9, 927, 2], [2, 3, 628]]

------------------------------ Epoch 167 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000007
- Iter 262 / 659, Loss: 0.000001
- Iter 393 / 659, Loss: 0.000027
- Iter 524 / 659, Loss: 0.000001
- Iter 655 / 659, Loss: 0.011753

* Train accuracy / confusion: 99.96% / [[8543, 1, 1], [3, 7484, 1], [1, 2, 5052]], 
* Val   accuracy / confusion: 99.28% / [[1064, 2, 3], [5, 930, 3], [3, 3, 627]]

------------------------------ Epoch 168 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000063
- Iter 262 / 659, Loss: 0.000016
- Iter 393 / 659, Loss: 0.000020
- Iter 524 / 659, Loss: 0.000061
- Iter 655 / 659, Loss: 0.000009

* Train accuracy / confusion: 99.94% / [[8536, 3, 1], [6, 7485, 0], [0, 2, 5055]], 
* Val   accuracy / confusion: 99.09% / [[1066, 1, 2], [10, 924, 4], [2, 5, 626]]

------------------------------ Epoch 169 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000035
- Iter 262 / 659, Loss: 0.000491
- Iter 393 / 659, Loss: 0.000005
- Iter 524 / 659, Loss: 0.000004
- Iter 655 / 659, Loss: 0.000832

* Train accuracy / confusion: 99.97% / [[8541, 1, 0], [3, 7489, 1], [1, 1, 5051]], 
* Val   accuracy / confusion: 99.20% / [[1064, 2, 3], [9, 927, 2], [3, 2, 628]]

------------------------------ Epoch 170 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000009
- Iter 262 / 659, Loss: 0.000598
- Iter 393 / 659, Loss: 0.000168
- Iter 524 / 659, Loss: 0.003724
- Iter 655 / 659, Loss: 0.000373

* Train accuracy / confusion: 99.94% / [[8534, 4, 1], [4, 7490, 1], [0, 2, 5052]], 
* Val   accuracy / confusion: 98.90% / [[1065, 2, 2], [9, 925, 4], [7, 5, 621]]

------------------------------ Epoch 171 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.001102
- Iter 262 / 659, Loss: 0.000002
- Iter 393 / 659, Loss: 0.000016
- Iter 524 / 659, Loss: 0.000013
- Iter 655 / 659, Loss: 0.000101

* Train accuracy / confusion: 99.97% / [[8543, 1, 2], [2, 7486, 1], [0, 0, 5053]], 
* Val   accuracy / confusion: 99.20% / [[1064, 4, 1], [7, 929, 2], [5, 2, 626]]

------------------------------ Epoch 172 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000004
- Iter 262 / 659, Loss: 0.000868
- Iter 393 / 659, Loss: 0.000002
- Iter 524 / 659, Loss: 0.000333
- Iter 655 / 659, Loss: 0.000016

* Train accuracy / confusion: 99.97% / [[8538, 4, 2], [1, 7491, 0], [0, 0, 5052]], 
* Val   accuracy / confusion: 99.05% / [[1064, 3, 2], [9, 925, 4], [2, 5, 626]]

------------------------------ Epoch 173 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000726
- Iter 262 / 659, Loss: 0.000438
- Iter 393 / 659, Loss: 0.000032
- Iter 524 / 659, Loss: 0.000015
- Iter 655 / 659, Loss: 0.000041

* Train accuracy / confusion: 99.95% / [[8542, 3, 0], [4, 7479, 3], [0, 0, 5057]], 
* Val   accuracy / confusion: 98.90% / [[1064, 3, 2], [12, 923, 3], [6, 3, 624]]

------------------------------ Epoch 174 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000042
- Iter 262 / 659, Loss: 0.000024
- Iter 393 / 659, Loss: 0.000019
- Iter 524 / 659, Loss: 0.000059
- Iter 655 / 659, Loss: 0.000327

* Train accuracy / confusion: 99.96% / [[8541, 4, 0], [1, 7490, 1], [0, 3, 5048]], 
* Val   accuracy / confusion: 99.02% / [[1061, 6, 2], [10, 924, 4], [2, 2, 629]]

------------------------------ Epoch 175 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000259
- Iter 262 / 659, Loss: 0.000037
- Iter 393 / 659, Loss: 0.000399
- Iter 524 / 659, Loss: 0.000004
- Iter 655 / 659, Loss: 0.000012

* Train accuracy / confusion: 99.96% / [[8538, 5, 2], [1, 7490, 1], [0, 0, 5051]], 
* Val   accuracy / confusion: 99.09% / [[1065, 2, 2], [10, 926, 2], [4, 4, 625]]

------------------------------ Epoch 176 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.001113
- Iter 262 / 659, Loss: 0.000011
- Iter 393 / 659, Loss: 0.000004
- Iter 524 / 659, Loss: 0.000018
- Iter 655 / 659, Loss: 0.000076

* Train accuracy / confusion: 99.96% / [[8543, 1, 0], [5, 7484, 1], [1, 1, 5052]], 
* Val   accuracy / confusion: 99.24% / [[1064, 4, 1], [10, 926, 2], [0, 3, 630]]

------------------------------ Epoch 177 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000012
- Iter 262 / 659, Loss: 0.000003
- Iter 393 / 659, Loss: 0.000024
- Iter 524 / 659, Loss: 0.000428
- Iter 655 / 659, Loss: 0.000016

* Train accuracy / confusion: 99.94% / [[8542, 3, 1], [3, 7484, 1], [3, 2, 5049]], 
* Val   accuracy / confusion: 99.05% / [[1063, 3, 3], [10, 925, 3], [3, 3, 627]]

------------------------------ Epoch 178 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000041
- Iter 262 / 659, Loss: 0.000208
- Iter 393 / 659, Loss: 0.000024
- Iter 524 / 659, Loss: 0.000261
- Iter 655 / 659, Loss: 0.000003

* Train accuracy / confusion: 99.97% / [[8542, 2, 1], [1, 7487, 2], [1, 0, 5052]], 
* Val   accuracy / confusion: 99.02% / [[1064, 3, 2], [11, 923, 4], [2, 4, 627]]

------------------------------ Epoch 179 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000104
- Iter 262 / 659, Loss: 0.000163
- Iter 393 / 659, Loss: 0.000036
- Iter 524 / 659, Loss: 0.000960
- Iter 655 / 659, Loss: 0.000037

* Train accuracy / confusion: 99.96% / [[8538, 3, 0], [5, 7489, 0], [0, 1, 5052]], 
* Val   accuracy / confusion: 99.02% / [[1065, 3, 1], [11, 923, 4], [4, 3, 626]]

------------------------------ Epoch 180 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.003236
- Iter 262 / 659, Loss: 0.000028
- Iter 393 / 659, Loss: 0.000007
- Iter 524 / 659, Loss: 0.000002
- Iter 655 / 659, Loss: 0.000350

* Train accuracy / confusion: 99.95% / [[8541, 4, 0], [4, 7485, 3], [0, 0, 5051]], 
* Val   accuracy / confusion: 99.13% / [[1064, 3, 2], [8, 928, 2], [5, 3, 625]]

------------------------------ Epoch 181 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000085
- Iter 262 / 659, Loss: 0.000198
- Iter 393 / 659, Loss: 0.000058
- Iter 524 / 659, Loss: 0.000007
- Iter 655 / 659, Loss: 0.000001

* Train accuracy / confusion: 99.98% / [[8547, 0, 0], [3, 7483, 0], [1, 0, 5054]], 
* Val   accuracy / confusion: 98.94% / [[1068, 0, 1], [12, 921, 5], [5, 5, 623]]

------------------------------ Epoch 182 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000028
- Iter 262 / 659, Loss: 0.000212
- Iter 393 / 659, Loss: 0.000055
- Iter 524 / 659, Loss: 0.000035
- Iter 655 / 659, Loss: 0.004505

* Train accuracy / confusion: 99.96% / [[8538, 4, 1], [0, 7490, 2], [1, 1, 5051]], 
* Val   accuracy / confusion: 98.86% / [[1063, 4, 2], [10, 924, 4], [7, 3, 623]]

------------------------------ Epoch 183 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000022
- Iter 262 / 659, Loss: 0.000090
- Iter 393 / 659, Loss: 0.000168
- Iter 524 / 659, Loss: 0.000019
- Iter 655 / 659, Loss: 0.001865

* Train accuracy / confusion: 99.99% / [[8543, 2, 0], [0, 7493, 0], [0, 0, 5050]], 
* Val   accuracy / confusion: 99.20% / [[1065, 2, 2], [10, 924, 4], [1, 2, 630]]

------------------------------ Epoch 184 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000011
- Iter 262 / 659, Loss: 0.000046
- Iter 393 / 659, Loss: 0.000003
- Iter 524 / 659, Loss: 0.000018
- Iter 655 / 659, Loss: 0.000001

* Train accuracy / confusion: 99.96% / [[8538, 2, 0], [1, 7486, 2], [1, 2, 5056]], 
* Val   accuracy / confusion: 98.98% / [[1064, 2, 3], [12, 924, 2], [6, 2, 625]]

------------------------------ Epoch 185 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000001
- Iter 262 / 659, Loss: 0.000039
- Iter 393 / 659, Loss: 0.000004
- Iter 524 / 659, Loss: 0.000006
- Iter 655 / 659, Loss: 0.000576

* Train accuracy / confusion: 99.96% / [[8540, 1, 1], [4, 7488, 1], [0, 2, 5051]], 
* Val   accuracy / confusion: 99.28% / [[1068, 0, 1], [8, 926, 4], [3, 3, 627]]

------------------------------ Epoch 186 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000006
- Iter 262 / 659, Loss: 0.000011
- Iter 393 / 659, Loss: 0.000120
- Iter 524 / 659, Loss: 0.000079
- Iter 655 / 659, Loss: 0.000001

* Train accuracy / confusion: 99.98% / [[8542, 0, 2], [1, 7489, 0], [2, 0, 5052]], 
* Val   accuracy / confusion: 99.02% / [[1065, 3, 1], [12, 921, 5], [2, 3, 628]]

------------------------------ Epoch 187 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.015817
- Iter 262 / 659, Loss: 0.000031
- Iter 393 / 659, Loss: 0.000322
- Iter 524 / 659, Loss: 0.000011
- Iter 655 / 659, Loss: 0.000235

* Train accuracy / confusion: 99.97% / [[8539, 0, 3], [2, 7493, 1], [0, 0, 5050]], 
* Val   accuracy / confusion: 99.09% / [[1067, 1, 1], [10, 925, 3], [7, 2, 624]]

------------------------------ Epoch 188 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000005
- Iter 262 / 659, Loss: 0.000022
- Iter 393 / 659, Loss: 0.004608
- Iter 524 / 659, Loss: 0.000054
- Iter 655 / 659, Loss: 0.001664

* Train accuracy / confusion: 99.99% / [[8545, 1, 0], [1, 7487, 0], [1, 0, 5053]], 
* Val   accuracy / confusion: 99.13% / [[1066, 1, 2], [10, 927, 1], [3, 6, 624]]

------------------------------ Epoch 189 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.020467
- Iter 262 / 659, Loss: 0.000073
- Iter 393 / 659, Loss: 0.000024
- Iter 524 / 659, Loss: 0.000060
- Iter 655 / 659, Loss: 0.067690

* Train accuracy / confusion: 99.97% / [[8539, 2, 0], [2, 7492, 0], [3, 0, 5050]], 
* Val   accuracy / confusion: 99.13% / [[1062, 5, 2], [7, 928, 3], [5, 1, 627]]

------------------------------ Epoch 190 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.018059
- Iter 262 / 659, Loss: 0.000028
- Iter 393 / 659, Loss: 0.000023
- Iter 524 / 659, Loss: 0.000003
- Iter 655 / 659, Loss: 0.000002

* Train accuracy / confusion: 99.96% / [[8538, 3, 0], [2, 7486, 2], [1, 1, 5055]], 
* Val   accuracy / confusion: 99.13% / [[1064, 4, 1], [10, 925, 3], [3, 2, 628]]

------------------------------ Epoch 191 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.001665
- Iter 262 / 659, Loss: 0.000014
- Iter 393 / 659, Loss: 0.000144
- Iter 524 / 659, Loss: 0.000045
- Iter 655 / 659, Loss: 0.000014

* Train accuracy / confusion: 99.97% / [[8543, 1, 1], [2, 7488, 2], [0, 1, 5050]], 
* Val   accuracy / confusion: 99.32% / [[1065, 2, 2], [7, 928, 3], [2, 2, 629]]

------------------------------ Epoch 192 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000015
- Iter 262 / 659, Loss: 0.000041
- Iter 393 / 659, Loss: 0.000404
- Iter 524 / 659, Loss: 0.000348
- Iter 655 / 659, Loss: 0.000008

* Train accuracy / confusion: 99.99% / [[8540, 1, 0], [1, 7490, 0], [0, 0, 5056]], 
* Val   accuracy / confusion: 99.20% / [[1066, 0, 3], [8, 929, 1], [7, 2, 624]]

------------------------------ Epoch 193 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000008
- Iter 262 / 659, Loss: 0.000002
- Iter 393 / 659, Loss: 0.000001
- Iter 524 / 659, Loss: 0.000023
- Iter 655 / 659, Loss: 0.002905

* Train accuracy / confusion: 99.98% / [[8542, 1, 0], [2, 7487, 2], [0, 0, 5054]], 
* Val   accuracy / confusion: 99.20% / [[1066, 2, 1], [7, 928, 3], [3, 5, 625]]

------------------------------ Epoch 194 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000016
- Iter 262 / 659, Loss: 0.000001
- Iter 393 / 659, Loss: 0.000008
- Iter 524 / 659, Loss: 0.000018
- Iter 655 / 659, Loss: 0.000024

* Train accuracy / confusion: 99.97% / [[8537, 4, 0], [1, 7489, 1], [1, 0, 5055]], 
* Val   accuracy / confusion: 99.17% / [[1064, 3, 2], [8, 926, 4], [3, 2, 628]]

------------------------------ Epoch 195 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000681
- Iter 262 / 659, Loss: 0.000004
- Iter 393 / 659, Loss: 0.000015
- Iter 524 / 659, Loss: 0.000201
- Iter 655 / 659, Loss: 0.000359

* Train accuracy / confusion: 99.96% / [[8537, 4, 1], [0, 7495, 0], [1, 2, 5048]], 
* Val   accuracy / confusion: 99.09% / [[1068, 0, 1], [13, 922, 3], [4, 3, 626]]

------------------------------ Epoch 196 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000005
- Iter 262 / 659, Loss: 0.000014
- Iter 393 / 659, Loss: 0.000047
- Iter 524 / 659, Loss: 0.000019
- Iter 655 / 659, Loss: 0.000012

* Train accuracy / confusion: 99.97% / [[8540, 0, 1], [3, 7489, 1], [0, 1, 5053]], 
* Val   accuracy / confusion: 98.94% / [[1066, 2, 1], [14, 921, 3], [3, 5, 625]]

------------------------------ Epoch 197 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000007
- Iter 262 / 659, Loss: 0.000062
- Iter 393 / 659, Loss: 0.000004
- Iter 524 / 659, Loss: 0.000000
- Iter 655 / 659, Loss: 0.000021

* Train accuracy / confusion: 99.95% / [[8540, 4, 0], [5, 7486, 1], [0, 1, 5051]], 
* Val   accuracy / confusion: 98.83% / [[1062, 5, 2], [14, 922, 2], [5, 3, 625]]

------------------------------ Epoch 198 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000150
- Iter 262 / 659, Loss: 0.000237
- Iter 393 / 659, Loss: 0.000006
- Iter 524 / 659, Loss: 0.000012
- Iter 655 / 659, Loss: 0.000302

* Train accuracy / confusion: 99.95% / [[8543, 3, 1], [2, 7485, 3], [0, 2, 5049]], 
* Val   accuracy / confusion: 99.09% / [[1066, 1, 2], [13, 923, 2], [2, 4, 627]]

------------------------------ Epoch 199 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000419
- Iter 262 / 659, Loss: 0.000010
- Iter 393 / 659, Loss: 0.000008
- Iter 524 / 659, Loss: 0.000086
- Iter 655 / 659, Loss: 0.000073

* Train accuracy / confusion: 99.97% / [[8544, 1, 0], [2, 7487, 1], [1, 1, 5051]], 
* Val   accuracy / confusion: 99.09% / [[1064, 3, 2], [9, 927, 2], [4, 4, 625]]

------------------------------ Epoch 200 / 200, Learning rate: 3.45e-05 ------------------------------
- Iter 131 / 659, Loss: 0.019324
- Iter 262 / 659, Loss: 0.000010
- Iter 393 / 659, Loss: 0.000082
- Iter 524 / 659, Loss: 0.000033
- Iter 655 / 659, Loss: 0.121692

* Train accuracy / confusion: 99.96% / [[8533, 2, 2], [2, 7490, 2], [0, 1, 5056]], 
* Val   accuracy / confusion: 99.13% / [[1065, 1, 3], [7, 928, 3], [7, 2, 624]]

**************************************** Training Ends ****************************************
- Test accuracy: 99.08%

- Confusion matrix:
 [[31914   141    15]
 [  332 27678   100]
 [   56    88 18816]]

print('- Debug table:')
pprint.pp(test_debug, indent=2, width=100)

- Debug table:
{ '14546': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00865039_170816'},
  '00719': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00164098_041214'},
  '30664': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01303196_020317'},
  '04086': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00498791_260916'},
  '05765': {'GT': 2, 'Acc': ' 66.67%', 'Pred': [4, 6, 20], 'edfname': '00623681_070415'},
  '19208': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01008809_021017'},
  '19516': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01019016_241115'},
  '36174': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01430323_060819'},
  '12726': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00796686_020819'},
  '31736': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01324136_201217'},
  '07979': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00673505_020419'},
  '18204': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00971724_030414'},
  '10785': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00740750_110315'},
  '17786': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00958455_200917'},
  '06324': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00634467_180717'},
  '12220': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00776486_121016'},
  '13629': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00824216_080416'},
  '04527': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00601765_231118'},
  '23994': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01154834_180417'},
  '03080': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00406530_310719'},
  '30733': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [2, 0, 28], 'edfname': '01303263_281116'},
  '23424': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01147097_121119'},
  '24505': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01164481_220615'},
  '18902': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00988618_270618'},
  '32089': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01334787_211117'},
  '26285': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01212635_270515'},
  '16435': {'GT': 2, 'Acc': ' 60.00%', 'Pred': [2, 10, 18], 'edfname': '00926040_061115'},
  '19711': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01026841_270116'},
  '02693': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00388436_200616'},
  '06604': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00642740_120918'},
  '28610': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01266829_070416'},
  '17149': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00951066_131217'},
  '25105': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01186214'},
  '03473': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00430821_100217'},
  '30539': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01300391_251116'},
  '27284': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01240949_191015'},
  '31301': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01312293_120417'},
  '25411': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01195277_151119'},
  '16248': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00916546_281114'},
  '23430': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01147097_121119'},
  '07382': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '00659706_220218'},
  '19978': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01035046_020715'},
  '32422': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01338710_261017'},
  '15256': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00888613_060819'},
  '23265': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01145402_161117'},
  '18116': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00968332_310518'},
  '14219': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00851302_261217'},
  '10932': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00743464_220316'},
  '27547': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '01242982_110416'},
  '27439': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01242449_230617'},
  '26232': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01211503_070415'},
  '09156': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00699594_011217'},
  '23730': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01150270_020617'},
  '06750': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00646518_240217'},
  '25601': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01200378_230117'},
  '10096': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00723144_180118'},
  '19457': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01018538_070514'},
  '28228': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01260350_090316'},
  '31069': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01307883_100217'},
  '28183': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01256391_310316'},
  '03547': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00433859_230519'},
  '26203': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01211467_070415'},
  '20941': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01086373_290415'},
  '18188': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00971724_030414'},
  '17148': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00951066_131217'},
  '14254': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00851511_200818'},
  '19266': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01010278_281116'},
  '13049': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00805584_060717'},
  '00610': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00160843_080219'},
  '22878': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01139946_120214'},
  '28828': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01272370_271217'},
  '12287': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00777684_300119'},
  '08086': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00675516_021019'},
  '03376': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00422970_151118'},
  '02676': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00385518_130815'},
  '06492': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00640147_140519'},
  '22749': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01139939_110214'},
  '25085': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01183615_201014'},
  '05069': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00609255_140417'},
  '25154': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01186934_200519'},
  '07529': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00664961_261218'},
  '27329': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 5, 0], 'edfname': '01241945_191115'},
  '27878': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01251650_090819'},
  '11091': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00747390_130918'},
  '16040': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00907205_241019'},
  '07121': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00654091_080618'},
  '26019': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01208998_240315'},
  '16147': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00912302_100415'},
  '34624': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01392274_020320'},
  '28960': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01274934'},
  '27971': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01252396_141216'},
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  '14428': {'GT': 2, 'Acc': ' 80.00%', 'Pred': [3, 3, 24], 'edfname': '00860983_271114'},
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  '36441': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01464624_011119'},
  '11358': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00753842_110517'},
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  '18762': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00987555_270717'},
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  '26066': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '01209181_250315'},
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  '24750': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01174220_021216'},
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  '03529': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00430974_050315'},
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  '01312': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00244987_050315'},
  '03994': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '00487637_220715'},
  '00540': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00141670_081217'},
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  '27871': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01251650_090819'},
  '20755': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01074533_071116'},
  '20901': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01086373_020615'},
  '06183': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00632465_150414'},
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  '34623': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01392274_020320'},
  '26682': {'GT': 0, 'Acc': ' 93.33%', 'Pred': [28, 2, 0], 'edfname': '01225123_070715'},
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  '08161': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00675516_190919'},
  '00349': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00115426_080219'},
  '04019': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00489204_290116'},
  '15927': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00906868_071216'},
  '17869': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00961326_010817'},
  '23461': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01147098_060515'},
  '11619': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00760780_141118'},
  '34741': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01392885_240718'},
  '12278': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00777684_300119'},
  '15430': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00891889_060717'},
  '19981': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01035046_020715'},
  '09386': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00707209_261219'},
  '30338': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01298112_271016'},
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  '01695': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00287432_110518'},
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  '07050': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00650788_280415'},
  '20435': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01063007_171115'},
  '20284': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01054491_080116'},
  '12666': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00792411_261018'},
  '30898': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01306354_051216'},
  '06503': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00640147_140519'},
  '32312': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01338557_190717'},
  '35102': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01403151_080319'},
  '02866': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00397252_160119'},
  '27423': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01242449_230617'},
  '04444': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00532450_220317'},
  '18869': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00988278_210915'},
  '15246': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00888613_060819'},
  '17304': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00952475_210518'},
  '32365': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01338642_170717'},
  '10709': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00739864_070717'},
  '14737': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00873142_220319'},
  '16883': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00945146_011214'},
  '17306': {'GT': 0, 'Acc': ' 63.33%', 'Pred': [19, 11, 0], 'edfname': '00952475_210518'},
  '02795': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00396115_070618'},
  '25610': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01200378_230117'},
  '11405': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00755441_151018'},
  '27530': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01242981_161115'},
  '26300': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01212635_270515'},
  '08388': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00678916_020718'},
  '03988': {'GT': 0, 'Acc': ' 70.00%', 'Pred': [21, 9, 0], 'edfname': '00487637_220715'},
  '19837': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01027623_260916'},
  '22067': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01134450_161019'},
  '18805': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00988278_020819'},
  '36059': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01429076_070819'},
  '03268': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00417769_100117'},
  '12331': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [2, 28, 0], 'edfname': '00779511_050319'},
  '20706': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01073130_191018'},
  '21859': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01131959_310118'},
  '05921': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00626957_071217'},
  '03927': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00484934_090514'},
  '08505': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00684221_101016'},
  '04850': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00606999_180817'},
  '17906': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00962086_240216'},
  '16145': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00912302_100415'},
  '32427': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01338710_261017'},
  '01996': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00315506_250715'},
  '20953': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01090425_290419'},
  '23493': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01147098_080414'},
  '21109': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01096498_270718'},
  '27027': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01235272_051115'},
  '26340': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [4, 26, 0], 'edfname': '01215115_270715'},
  '10045': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00722511_031117'},
  '06378': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00635487_161019'},
  '09873': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00718764_270715'},
  '01766': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00293228_070918'},
  '20470': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01063145_220915'},
  '26161': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01209324_011217'},
  '16677': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00935467_100518'},
  '31614': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01321591_170417'},
  '12311': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00779318_101117'},
  '28787': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01271175_300516'},
  '25581': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01200248_290120'},
  '04808': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00606004_110518'},
  '19347': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01011144_070814'},
  '14700': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00872871_140618'},
  '08940': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00695058_191017'},
  '24426': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01163321_190718'},
  '15766': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00899964_060115'},
  '23310': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01145491_110314'},
  '14594': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00867679_291117'},
  '03332': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00418981_090316'},
  '19464': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01018538_070514'},
  '01507': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00265634_301117'},
  '29466': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01282315_040716'},
  '12665': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00792411_261018'},
  '31251': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01311956_130117'},
  '00123': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00048377_070916'},
  '01462': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00258008_230617'},
  '17836': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [1, 0, 29], 'edfname': '00960161_201014'},
  '24483': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01163965_130718'},
  '08680': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00685853_200618'},
  '00352': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00115426_080219'},
  '01981': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00314911_101218'},
  '14252': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00851511_200818'},
  '06240': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00634366_150316'},
  '36415': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01457795_141019'},
  '14823': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00875350_140514'},
  '16174': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00916265_090818'},
  '08014': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00674106_270819'},
  '03439': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00429185_130917'},
  '13424': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00817022_010415'},
  '09917': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00720119_200318'},
  '25869': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01203444_240719'},
  '01336': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00248994_171016'},
  '28146': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01254424_120118'},
  '04587': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00602435_270217'},
  '21482': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01124522_170818'},
  '20359': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01057134_240216'},
  '26885': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01235034_021117'},
  '00535': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00141670_081217'},
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  '24494': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01164481_220615'},
  '34803': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01395459_210819'},
  '06425': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00635487_301019'},
  '27114': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01235281_191015'},
  '00668': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00164098_021019'},
  '23102': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01141787_260315'},
  '17227': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00951808_251116'},
  '17610': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00956048_290715'},
  '34945': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01400560_160419'},
  '36081': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01429374_230519'},
  '14975': {'GT': 1, 'Acc': ' 30.00%', 'Pred': [21, 9, 0], 'edfname': '00883719_240812'},
  '15298': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00888613_110518'},
  '08856': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00692685_200919'},
  '18815': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00988278_020819'},
  '32716': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01344212_240817'},
  '27781': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01249485_110316'},
  '21992': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01133832_010716'},
  '14739': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00873142_220319'},
  '11013': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00745209_041018'},
  '18246': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00977111_211218'},
  '10284': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00731508_280817'},
  '30151': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01296344_211116'},
  '21827': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01130231_241117'},
  '23557': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01147101_190314'},
  '16545': {'GT': 0, 'Acc': ' 60.00%', 'Pred': [18, 10, 2], 'edfname': '00929175_100214'},
  '23080': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01141787_190515'},
  '23419': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01147097_121119'},
  '26236': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01211503_070415'},
  '05179': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00613032_281215'},
  '10826': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00741780_281116'},
  '21469': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01123822_271017'},
  '18296': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00978066_170815'},
  '30317': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01297810_151116'},
  '18423': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00980362_020616'},
  '10699': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00739864_070717'},
  '14434': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00860983_271114'},
  '08529': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00684846_030717'},
  '10983': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00744618_290319'},
  '18639': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00986061_130716'},
  '07142': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00654091_080618'},
  '32315': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01338557_190717'},
  '00285': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00088513_120717'},
  '08653': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00685248_310315'},
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  '14346': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00856756_131119'},
  '22188': {'GT': 0, 'Acc': ' 90.00%', 'Pred': [27, 3, 0], 'edfname': '01135534_230715'},
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  '17701': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00957827_230317'},
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  '25613': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01200378_230117'},
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  '36190': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01430323_060819'},
  '30633': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01302961_060117'},
  '25591': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01200378_230117'},
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  '26217': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01211467_120515'},
  '08952': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00695058_191017'},
  '17212': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00951808_251116'},
  '16200': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00916265_090818'},
  '07375': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [0, 28, 2], 'edfname': '00659706_220218'},
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  '15194': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00887757_041116'},
  '22877': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01139946_120214'},
  '20530': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01064740_231214'},
  '08528': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00684846_030717'},
  '03621': {'GT': 0, 'Acc': ' 76.67%', 'Pred': [23, 7, 0], 'edfname': '00456090_140417'},
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  '35157': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01404500_080219'},
  '03129': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00407878_310517'},
  '02833': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00396894_030119'},
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  '16590': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00931073_181018'},
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  '05055': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00609184_070618'},
  '05387': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00616379_010915'},
  '16156': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00915077_310818'},
  '24290': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01158725_220514'},
  '10708': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00739864_070717'},
  '18197': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00971724_030414'},
  '12345': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00779511_050319'},
  '29198': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01277998_220217'},
  '05324': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00615909_140819'},
  '28855': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01272370_271217'},
  '01611': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00286931_201217'},
  '23366': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01146683_300419'},
  '00359': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00115426_080219'},
  '06983': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00649687_180919'},
  '10755': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00740694_110315'},
  '20539': {'GT': 2, 'Acc': ' 10.00%', 'Pred': [26, 1, 3], 'edfname': '01064740_261015'},
  '29728': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01288482_220816'},
  '14333': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00853892_020519'},
  '09190': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00699668_200718'},
  '06865': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00649463_121219'},
  '04264': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00507286_010915'},
  '15761': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00899964_060115'},
  '15987': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00906868_231215'},
  '00675': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00164098_021019'},
  '29856': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01290676_090617'},
  '35049': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01402378_110219'},
  '30211': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01296557_010819'},
  '26570': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01220269_141218'},
  '19812': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01027623_060317'},
  '12464': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00784417_100315'},
  '12845': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00797248_130514'},
  '23572': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01147102_080414'},
  '15008': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00885321_170314'},
  '30663': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01303196_020317'},
  '11142': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00747390_250718'},
  '26688': {'GT': 0, 'Acc': ' 80.00%', 'Pred': [24, 6, 0], 'edfname': '01225123_070715'},
  '35021': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01402179_311018'},
  '31391': {'GT': 1, 'Acc': ' 70.00%', 'Pred': [9, 21, 0], 'edfname': '01314786_200317'},
  '29832': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01289759_110517'},
  '32951': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01348263_210917'},
  '16473': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00926040_121219'},
  '01125': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00232310_261218'},
  '00028': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '00029426_020817'},
  '08295': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00676993_310315'},
  '07491': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00663561_030414'},
  '01994': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00315506_250715'},
  '27396': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01241947_301215'},
  '02280': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00335823_190815'},
  '09459': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00708411_091118'},
  '13481': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00817092_020118'},
  '16518': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00926452_230318'},
  '34246': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01377415_200418'},
  '03778': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00474445_160414'},
  '25239': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01190283_261114'},
  '11798': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00764690_050319'},
  '22621': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01139924_060417'},
  '03089': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00406530_310719'},
  '00091': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00048377_070819'},
  '32046': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01334570_070717'},
  '10989': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00744618_290319'},
  '27319': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01241945_191115'},
  '20504': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01064650_161117'},
  '01926': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00313383_180314'},
  '25354': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [3, 27, 0], 'edfname': '01193508_171214'},
  '07657': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00666711_140218'},
  '27262': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01240162_121015'},
  '22180': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01135534_230715'},
  '01483': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00265504_101016'},
  '03669': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00458897_141016'},
  '11652': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00761575_090418'},
  '27085': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01235273_051015'},
  '25913': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01203444_251017'},
  '01478': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00265504_101016'},
  '12110': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00773108_250615'},
  '08126': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00675516_090120'},
  '30140': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01296344_211116'},
  '30504': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01300390_251116'},
  '22392': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01137650_290819'},
  '00502': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00138259_030518'},
  '09043': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00697249_090414'},
  '12495': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00784418_201016'},
  '05155': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00612060_221118'},
  '07948': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00673491_141014'},
  '12455': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00783696_220414'},
  '10290': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00731508_280817'},
  '04992': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00608836_270219'},
  '12108': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00773108_250615'},
  '27481': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01242661_271015'},
  '22835': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '01139944_040214'},
  '13461': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00817092_020118'},
  '02773': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00395714_170915'},
  '02178': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00326875_050314'},
  '02892': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00397252_160119'},
  '15586': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00896386_240914'},
  '09383': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00707209_261219'},
  '15739': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00899695_280918'},
  '07284': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00657399_070515'},
  '15935': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00906868_071216'},
  '17823': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00959479_090217'},
  '20369': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01057134_310316'},
  '01973': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00314911_101218'},
  '35402': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01410438_241218'},
  '00652': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00162025_221215'},
  '24102': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01155731_070514'},
  '05162': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00613032_281215'},
  '33684': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01364379_230118'},
  '06898': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00649687_010715'},
  '22205': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01135545_160117'},
  '31605': {'GT': 2, 'Acc': ' 93.33%', 'Pred': [1, 1, 28], 'edfname': '01321591_170417'},
  '22920': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01139948_290114'},
  '01402': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00257311_221018'},
  '33073': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01351393_101019'},
  '22184': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01135534_230715'},
  '14930': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00879020_080817'},
  '32679': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01344212_220819'},
  '14230': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00851511_101018'},
  '27896': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01251650_191219'},
  '11640': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00760920_270418'},
  '09888': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00720119_200318'},
  '33713': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01364379_260919'},
  '23649': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01149075_090414'},
  '25768': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01201484_150618'},
  '16806': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00939957_220517'},
  '21575': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01126091_200515'},
  '08638': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00685248_150414'},
  '02065': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00317645_311016'},
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  '23233': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01144046_260517'},
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  '36437': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01464624_011119'},
  '18059': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00966506_050419'},
  '09169': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00699594_011217'},
  '12053': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00771910_121016'},
  '01773': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00293228_070918'},
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  '22728': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01139924_300315'},
  '34682': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01392607_260718'},
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  '18365': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '00978569_251018'},
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  '09871': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00718764_270715'},
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  '14934': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00879020_080817'},
  '18949': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00996941_180517'},
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  '34189': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01376302_151019'},
  '20134': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01041208_020818'},
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  '12901': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00803113_260516'},
  '30025': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01295899_041016'},
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  '13000': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00803780_071218'},
  '04260': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00507286_010915'},
  '32484': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01339759_071119'},
  '32387': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01338642_170717'},
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  '06304': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00634366_311019'},
  '07598': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00665821_260815'},
  '33056': {'GT': 1, 'Acc': ' 86.67%', 'Pred': [4, 26, 0], 'edfname': '01351393_101019'},
  '10698': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00739864_070717'},
  '03608': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00450712_161118'},
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  '30343': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01298112_271016'},
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  '27842': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01251650_060116'},
  '00043': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00029426_020817'},
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  '00436': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00127458_150618'},
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  '06278': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00634366_241019'},
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  '16484': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [0, 1, 29], 'edfname': '00926040_201119'},
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  '34465': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01382697_261218'},
  '28391': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01264838_110416'},
  '17663': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00956561_241116'},
  '20524': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01064740_231214'},
  '15333': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00889595_240419'},
  '22255': {'GT': 1, 'Acc': '  0.00%', 'Pred': [30, 0, 0], 'edfname': '01135545_230715'},
  '30251': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01296557_160120'},
  '08835': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00692685_110919'},
  '08729': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00687918_020318'},
  '20945': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01090425_290419'},
  '30129': {'GT': 1, 'Acc': ' 83.33%', 'Pred': [5, 25, 0], 'edfname': '01296342_141116'},
  '34095': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01372947_240518'},
  '28477': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01264980_200516'},
  '20492': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01064650_161117'},
  '18695': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00986061_270116'},
  '18532': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00984999_070617'},
  '16966': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00948785_120116'},
  '19729': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01026990_241215'},
  '12469': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00784417_100315'},
  '04286': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00507286_270715'},
  '08571': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00685191_100419'},
  '32107': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01334787_211117'},
  '29409': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01280762_171116'},
  '11460': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00758349_270418'},
  '09992': {'GT': 1, 'Acc': ' 96.67%', 'Pred': [1, 29, 0], 'edfname': '00722143_261118'},
  '28686': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01269786_071019'},
  '00420': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00126875_260515'},
  '31013': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01306737_151216'},
  '01003': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00213418_210115'},
  '07285': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00657399_070515'},
  '21102': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01096498_270718'},
  '32014': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01333917_210918'},
  '08955': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00695058_191017'},
  '01995': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00315506_250715'},
  '16694': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00935598_301018'},
  '04320': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00518633_311215'},
  '11168': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00752045_260419'},
  '31307': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01312293_120417'},
  '21114': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01096498_270718'},
  '30581': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01301982_151116'}}

Small ResNet-like Model

model = ResNet(block=BasicResBlock, conv_layers=[1, 1, 1, 1], n_fc=3, 
               n_input=train_dataset[0]['signal'].shape[0], n_output=3, n_start=64,
               kernel_size=9, use_age=False)
model = model.to(device, dtype=torch.float32)
print(model)
print()

n = count_parameters(model)
print(f'The Number of parameters of the model: {n:,}')

ResNet(
  (input_stage): Sequential(
    (0): Conv1d(20, 64, kernel_size=(27,), stride=(3,), padding=(13,), bias=False)
    (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU()
  )
  (conv_stage1): Sequential(
    (0): BasicResBlock(
      (conv1): Conv1d(64, 64, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)
      (bn1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv1d(64, 64, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)
      (bn2): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (1): MaxPool1d(kernel_size=3, stride=3, padding=0, dilation=1, ceil_mode=False)
  )
  (conv_stage2): Sequential(
    (0): BasicResBlock(
      (conv1): Conv1d(64, 128, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)
      (bn1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv1d(128, 128, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)
      (bn2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (downsample): Sequential(
        (0): Conv1d(64, 128, kernel_size=(1,), stride=(1,), bias=False)
        (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): MaxPool1d(kernel_size=3, stride=3, padding=0, dilation=1, ceil_mode=False)
  )
  (conv_stage3): Sequential(
    (0): BasicResBlock(
      (conv1): Conv1d(128, 256, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)
      (bn1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv1d(256, 256, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)
      (bn2): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (downsample): Sequential(
        (0): Conv1d(128, 256, kernel_size=(1,), stride=(1,), bias=False)
        (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): MaxPool1d(kernel_size=3, stride=3, padding=0, dilation=1, ceil_mode=False)
  )
  (conv_stage4): Sequential(
    (0): BasicResBlock(
      (conv1): Conv1d(256, 512, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)
      (bn1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv1d(512, 512, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)
      (bn2): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (downsample): Sequential(
        (0): Conv1d(256, 512, kernel_size=(1,), stride=(1,), bias=False)
        (1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): MaxPool1d(kernel_size=3, stride=3, padding=0, dilation=1, ceil_mode=False)
  )
  (final_pool): AdaptiveAvgPool1d(output_size=1)
  (fc_stage): Sequential(
    (0): Sequential(
      (0): Linear(in_features=512, out_features=256, bias=False)
      (1): Dropout(p=0.1, inplace=False)
      (2): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (3): ReLU()
    )
    (1): Sequential(
      (0): Linear(in_features=256, out_features=128, bias=False)
      (1): Dropout(p=0.1, inplace=False)
      (2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (3): ReLU()
    )
    (2): Sequential(
      (0): Linear(in_features=128, out_features=64, bias=False)
      (1): Dropout(p=0.1, inplace=False)
      (2): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (3): ReLU()
    )
    (3): Linear(in_features=64, out_features=3, bias=True)
  )
)

The Number of parameters of the model: 5,104,067

# record = learning_rate_search(model, 
#                               min_log_lr=-4.5, 
#                               max_log_lr=-1.0, 
#                               trials=500, 
#                               epochs=1)
# draw_learning_rate_record(record)
# best_log_lr = record[np.argmax(np.array([v for lr, v in record]))][0]

best_log_lr = -2.3
print('best_log_lr:', best_log_lr)

best_log_lr: -2.3

# reduce the learning after [lr_schedule_step] epochs by a factor of 10
n_epoch = 200
lr_schedule_step = 80
log_interval = len(train_loader) // 5

loss_history = []
train_acc_history = []
val_acc_history = []

model.reset_weights()
optimizer = optim.AdamW(model.parameters(), lr=10 ** best_log_lr, weight_decay=0.0001)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=lr_schedule_step, gamma=0.1)

print(f'{"*"*40} Training Starts {"*"*40}')

for epoch in tqdm(range(1, n_epoch + 1)):
    print(f'{"-"*30} Epoch {epoch:03d} / {n_epoch:03d}, Learning rate: {optimizer.param_groups[-1]["lr"]:.2e} {"-"*30}')
    
    # train
    loss, train_accuracy, train_confusion = train_one_epoch(model, optimizer, log_interval)
    loss_history.extend(loss)
    train_acc_history.append(train_accuracy)
    
    # validation
    val_accuracy, val_confusion = check_val_accuracy(model)
    val_acc_history.append(val_accuracy)
    
    # learning rate schedule
    scheduler.step()
    
    print()
    print(f'* Train accuracy / confusion: {train_accuracy:.2f}% / {train_confusion.tolist()}, ')
    print(f'* Val   accuracy / confusion: {val_accuracy:.2f}% / {val_confusion.tolist()}')
    print()
    
# test
test_accuracy, test_confusion, test_debug = check_test_accuracy(model, repeat=30)

print(f'{"*"*40} Training Ends {"*"*40}')
print(f'- Test accuracy: {test_accuracy:.2f}%')
print()
print('- Confusion matrix:\n', test_confusion)
print()

# draw the training loss plot
draw_loss_plot(loss_history)
draw_accuracy_history(train_acc_history, val_acc_history)

# draw the confusion matrix
draw_confusion(test_confusion)

**************************************** Training Starts ****************************************

------------------------------ Epoch 001 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 1.045403
- Iter 262 / 659, Loss: 0.993572
- Iter 393 / 659, Loss: 0.874145
- Iter 524 / 659, Loss: 1.004832
- Iter 655 / 659, Loss: 1.003667

* Train accuracy / confusion: 46.43% / [[5922, 2166, 457], [3731, 2739, 1022], [1741, 2179, 1131]], 
* Val   accuracy / confusion: 52.23% / [[882, 85, 102], [467, 118, 353], [176, 78, 379]]

------------------------------ Epoch 002 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.972686
- Iter 262 / 659, Loss: 1.027216
- Iter 393 / 659, Loss: 1.179174
- Iter 524 / 659, Loss: 1.036236
- Iter 655 / 659, Loss: 0.837494

* Train accuracy / confusion: 52.93% / [[6252, 1852, 437], [3044, 2842, 1603], [927, 2064, 2067]], 
* Val   accuracy / confusion: 51.06% / [[1007, 57, 5], [639, 233, 66], [290, 235, 108]]

------------------------------ Epoch 003 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 1.021527
- Iter 262 / 659, Loss: 0.957936
- Iter 393 / 659, Loss: 0.939250
- Iter 524 / 659, Loss: 0.989099
- Iter 655 / 659, Loss: 0.974481

* Train accuracy / confusion: 56.69% / [[6384, 1789, 370], [2852, 3127, 1510], [798, 1814, 2444]], 
* Val   accuracy / confusion: 55.80% / [[671, 395, 3], [191, 732, 15], [42, 521, 70]]

------------------------------ Epoch 004 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.936524
- Iter 262 / 659, Loss: 0.719075
- Iter 393 / 659, Loss: 0.936428
- Iter 524 / 659, Loss: 0.916245
- Iter 655 / 659, Loss: 0.776660

* Train accuracy / confusion: 60.49% / [[6526, 1610, 406], [2636, 3532, 1323], [746, 1610, 2699]], 
* Val   accuracy / confusion: 60.91% / [[766, 205, 98], [239, 399, 300], [66, 124, 443]]

------------------------------ Epoch 005 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.859263
- Iter 262 / 659, Loss: 0.745908
- Iter 393 / 659, Loss: 0.867735
- Iter 524 / 659, Loss: 0.891057
- Iter 655 / 659, Loss: 0.836498

* Train accuracy / confusion: 62.72% / [[6526, 1635, 380], [2516, 3761, 1216], [603, 1512, 2939]], 
* Val   accuracy / confusion: 61.82% / [[610, 430, 29], [129, 727, 82], [35, 303, 295]]

------------------------------ Epoch 006 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 1.065164
- Iter 262 / 659, Loss: 0.800072
- Iter 393 / 659, Loss: 0.650146
- Iter 524 / 659, Loss: 0.678440
- Iter 655 / 659, Loss: 0.975123

* Train accuracy / confusion: 64.61% / [[6447, 1695, 397], [2279, 4056, 1156], [554, 1383, 3121]], 
* Val   accuracy / confusion: 65.87% / [[831, 206, 32], [258, 507, 173], [95, 137, 401]]

------------------------------ Epoch 007 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.756223
- Iter 262 / 659, Loss: 0.613976
- Iter 393 / 659, Loss: 0.889924
- Iter 524 / 659, Loss: 0.746476
- Iter 655 / 659, Loss: 0.637998

* Train accuracy / confusion: 66.66% / [[6597, 1580, 364], [2165, 4267, 1059], [546, 1316, 3194]], 
* Val   accuracy / confusion: 67.69% / [[803, 238, 28], [227, 589, 122], [81, 157, 395]]

------------------------------ Epoch 008 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.677678
- Iter 262 / 659, Loss: 0.851059
- Iter 393 / 659, Loss: 0.650802
- Iter 524 / 659, Loss: 0.570197
- Iter 655 / 659, Loss: 0.624485

* Train accuracy / confusion: 68.73% / [[6753, 1449, 340], [2127, 4337, 1027], [472, 1180, 3403]], 
* Val   accuracy / confusion: 54.02% / [[571, 169, 329], [103, 251, 584], [12, 17, 604]]

------------------------------ Epoch 009 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.467161
- Iter 262 / 659, Loss: 0.687599
- Iter 393 / 659, Loss: 0.649060
- Iter 524 / 659, Loss: 1.184904
- Iter 655 / 659, Loss: 0.595144

* Train accuracy / confusion: 70.80% / [[6757, 1386, 399], [1968, 4558, 964], [483, 958, 3615]], 
* Val   accuracy / confusion: 70.87% / [[715, 299, 55], [132, 706, 100], [20, 163, 450]]

------------------------------ Epoch 010 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.537220
- Iter 262 / 659, Loss: 0.706623
- Iter 393 / 659, Loss: 0.550630
- Iter 524 / 659, Loss: 0.772135
- Iter 655 / 659, Loss: 0.581779

* Train accuracy / confusion: 73.60% / [[6941, 1204, 399], [1787, 4802, 906], [452, 820, 3777]], 
* Val   accuracy / confusion: 73.11% / [[787, 174, 108], [146, 577, 215], [26, 41, 566]]

------------------------------ Epoch 011 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.482269
- Iter 262 / 659, Loss: 0.534537
- Iter 393 / 659, Loss: 0.577456
- Iter 524 / 659, Loss: 0.468435
- Iter 655 / 659, Loss: 0.486416

* Train accuracy / confusion: 76.88% / [[7058, 1123, 363], [1510, 5254, 727], [401, 751, 3901]], 
* Val   accuracy / confusion: 71.14% / [[603, 380, 86], [53, 824, 61], [10, 172, 451]]

------------------------------ Epoch 012 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.455410
- Iter 262 / 659, Loss: 0.564202
- Iter 393 / 659, Loss: 0.366771
- Iter 524 / 659, Loss: 0.284585
- Iter 655 / 659, Loss: 0.332062

* Train accuracy / confusion: 79.82% / [[7235, 984, 321], [1340, 5509, 641], [355, 615, 4088]], 
* Val   accuracy / confusion: 76.36% / [[796, 237, 36], [106, 771, 61], [35, 149, 449]]

------------------------------ Epoch 013 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.431924
- Iter 262 / 659, Loss: 0.337481
- Iter 393 / 659, Loss: 0.448526
- Iter 524 / 659, Loss: 0.426657
- Iter 655 / 659, Loss: 0.320429

* Train accuracy / confusion: 82.41% / [[7376, 894, 272], [1129, 5842, 520], [348, 546, 4161]], 
* Val   accuracy / confusion: 72.73% / [[1051, 13, 5], [439, 486, 13], [162, 88, 383]]

------------------------------ Epoch 014 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.319209
- Iter 262 / 659, Loss: 0.270460
- Iter 393 / 659, Loss: 0.551788
- Iter 524 / 659, Loss: 0.304741
- Iter 655 / 659, Loss: 0.251981

* Train accuracy / confusion: 85.09% / [[7555, 728, 257], [996, 6058, 435], [314, 415, 4330]], 
* Val   accuracy / confusion: 70.87% / [[839, 230, 0], [59, 876, 3], [76, 401, 156]]

------------------------------ Epoch 015 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.415394
- Iter 262 / 659, Loss: 0.307076
- Iter 393 / 659, Loss: 0.367754
- Iter 524 / 659, Loss: 0.408083
- Iter 655 / 659, Loss: 0.166064

* Train accuracy / confusion: 87.00% / [[7630, 690, 227], [814, 6289, 385], [262, 363, 4428]], 
* Val   accuracy / confusion: 84.89% / [[1048, 15, 6], [222, 678, 38], [86, 32, 515]]

------------------------------ Epoch 016 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.323646
- Iter 262 / 659, Loss: 0.332627
- Iter 393 / 659, Loss: 0.100795
- Iter 524 / 659, Loss: 0.164220
- Iter 655 / 659, Loss: 0.221822

* Train accuracy / confusion: 88.75% / [[7753, 586, 202], [744, 6446, 302], [236, 303, 4516]], 
* Val   accuracy / confusion: 89.58% / [[936, 113, 20], [40, 860, 38], [15, 49, 569]]

------------------------------ Epoch 017 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.289508
- Iter 262 / 659, Loss: 0.317514
- Iter 393 / 659, Loss: 0.083257
- Iter 524 / 659, Loss: 0.219092
- Iter 655 / 659, Loss: 0.279570

* Train accuracy / confusion: 89.76% / [[7808, 553, 182], [668, 6534, 287], [193, 277, 4586]], 
* Val   accuracy / confusion: 89.66% / [[985, 63, 21], [71, 847, 20], [33, 65, 535]]

------------------------------ Epoch 018 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.302679
- Iter 262 / 659, Loss: 0.299198
- Iter 393 / 659, Loss: 0.203580
- Iter 524 / 659, Loss: 0.096859
- Iter 655 / 659, Loss: 0.403843

* Train accuracy / confusion: 91.06% / [[7905, 463, 173], [588, 6651, 250], [183, 228, 4647]], 
* Val   accuracy / confusion: 85.42% / [[782, 248, 39], [17, 886, 35], [7, 39, 587]]

------------------------------ Epoch 019 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.193648
- Iter 262 / 659, Loss: 0.116363
- Iter 393 / 659, Loss: 0.080785
- Iter 524 / 659, Loss: 0.060210
- Iter 655 / 659, Loss: 0.174194

* Train accuracy / confusion: 92.28% / [[8033, 381, 132], [516, 6746, 228], [162, 208, 4682]], 
* Val   accuracy / confusion: 88.75% / [[1055, 10, 4], [160, 739, 39], [67, 17, 549]]

------------------------------ Epoch 020 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.082811
- Iter 262 / 659, Loss: 0.277709
- Iter 393 / 659, Loss: 0.119079
- Iter 524 / 659, Loss: 0.137427
- Iter 655 / 659, Loss: 0.193202

* Train accuracy / confusion: 92.83% / [[8028, 375, 140], [472, 6849, 172], [158, 195, 4699]], 
* Val   accuracy / confusion: 92.84% / [[972, 60, 37], [30, 875, 33], [8, 21, 604]]

------------------------------ Epoch 021 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.078169
- Iter 262 / 659, Loss: 0.140872
- Iter 393 / 659, Loss: 0.198405
- Iter 524 / 659, Loss: 0.381499
- Iter 655 / 659, Loss: 0.188599

* Train accuracy / confusion: 93.86% / [[8103, 321, 119], [392, 6916, 182], [121, 159, 4775]], 
* Val   accuracy / confusion: 91.59% / [[968, 95, 6], [24, 899, 15], [16, 66, 551]]

------------------------------ Epoch 022 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.185468
- Iter 262 / 659, Loss: 0.073835
- Iter 393 / 659, Loss: 0.208423
- Iter 524 / 659, Loss: 0.030136
- Iter 655 / 659, Loss: 0.095710

* Train accuracy / confusion: 94.29% / [[8139, 298, 108], [363, 6958, 167], [135, 134, 4786]], 
* Val   accuracy / confusion: 93.26% / [[1051, 12, 6], [79, 842, 17], [39, 25, 569]]

------------------------------ Epoch 023 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.070658
- Iter 262 / 659, Loss: 0.076164
- Iter 393 / 659, Loss: 0.317943
- Iter 524 / 659, Loss: 0.062189
- Iter 655 / 659, Loss: 0.083500

* Train accuracy / confusion: 94.53% / [[8153, 286, 101], [360, 6966, 165], [109, 133, 4815]], 
* Val   accuracy / confusion: 94.05% / [[999, 65, 5], [23, 900, 15], [13, 36, 584]]

------------------------------ Epoch 024 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.174285
- Iter 262 / 659, Loss: 0.115163
- Iter 393 / 659, Loss: 0.092289
- Iter 524 / 659, Loss: 0.060355
- Iter 655 / 659, Loss: 0.009286

* Train accuracy / confusion: 95.05% / [[8167, 276, 97], [280, 7065, 145], [105, 140, 4813]], 
* Val   accuracy / confusion: 91.10% / [[1041, 12, 16], [109, 761, 68], [12, 18, 603]]

------------------------------ Epoch 025 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.033505
- Iter 262 / 659, Loss: 0.032752
- Iter 393 / 659, Loss: 0.135844
- Iter 524 / 659, Loss: 0.117900
- Iter 655 / 659, Loss: 0.166294

* Train accuracy / confusion: 95.40% / [[8209, 246, 90], [292, 7068, 130], [89, 122, 4842]], 
* Val   accuracy / confusion: 92.73% / [[1046, 21, 2], [79, 853, 6], [54, 30, 549]]

------------------------------ Epoch 026 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.276578
- Iter 262 / 659, Loss: 0.179345
- Iter 393 / 659, Loss: 0.315461
- Iter 524 / 659, Loss: 0.214710
- Iter 655 / 659, Loss: 0.123425

* Train accuracy / confusion: 95.88% / [[8264, 196, 83], [282, 7096, 113], [77, 118, 4859]], 
* Val   accuracy / confusion: 93.48% / [[953, 77, 39], [6, 890, 42], [1, 7, 625]]

------------------------------ Epoch 027 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.044651
- Iter 262 / 659, Loss: 0.016725
- Iter 393 / 659, Loss: 0.097641
- Iter 524 / 659, Loss: 0.123788
- Iter 655 / 659, Loss: 0.115165

* Train accuracy / confusion: 96.17% / [[8257, 211, 74], [231, 7136, 121], [78, 93, 4887]], 
* Val   accuracy / confusion: 91.40% / [[964, 22, 83], [40, 833, 65], [4, 13, 616]]

------------------------------ Epoch 028 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.073059
- Iter 262 / 659, Loss: 0.018499
- Iter 393 / 659, Loss: 0.347205
- Iter 524 / 659, Loss: 0.066453
- Iter 655 / 659, Loss: 0.093383

* Train accuracy / confusion: 96.50% / [[8284, 192, 67], [239, 7153, 95], [68, 78, 4912]], 
* Val   accuracy / confusion: 95.68% / [[1024, 35, 10], [34, 886, 18], [2, 15, 616]]

------------------------------ Epoch 029 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.239836
- Iter 262 / 659, Loss: 0.062434
- Iter 393 / 659, Loss: 0.284020
- Iter 524 / 659, Loss: 0.049440
- Iter 655 / 659, Loss: 0.142407

* Train accuracy / confusion: 96.76% / [[8322, 152, 66], [211, 7185, 94], [73, 88, 4897]], 
* Val   accuracy / confusion: 96.44% / [[1037, 29, 3], [27, 904, 7], [9, 19, 605]]

------------------------------ Epoch 030 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.093039
- Iter 262 / 659, Loss: 0.149218
- Iter 393 / 659, Loss: 0.099343
- Iter 524 / 659, Loss: 0.065982
- Iter 655 / 659, Loss: 0.085671

* Train accuracy / confusion: 96.78% / [[8314, 167, 63], [217, 7183, 88], [61, 84, 4911]], 
* Val   accuracy / confusion: 92.88% / [[949, 74, 46], [22, 888, 28], [4, 14, 615]]

------------------------------ Epoch 031 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.053022
- Iter 262 / 659, Loss: 0.028489
- Iter 393 / 659, Loss: 0.039535
- Iter 524 / 659, Loss: 0.034040
- Iter 655 / 659, Loss: 0.012992

* Train accuracy / confusion: 97.18% / [[8358, 132, 56], [190, 7217, 85], [51, 81, 4918]], 
* Val   accuracy / confusion: 93.33% / [[957, 105, 7], [6, 927, 5], [6, 47, 580]]

------------------------------ Epoch 032 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.061373
- Iter 262 / 659, Loss: 0.124569
- Iter 393 / 659, Loss: 0.090656
- Iter 524 / 659, Loss: 0.259266
- Iter 655 / 659, Loss: 0.136550

* Train accuracy / confusion: 97.28% / [[8332, 153, 59], [167, 7251, 71], [61, 62, 4932]], 
* Val   accuracy / confusion: 92.54% / [[921, 123, 25], [4, 922, 12], [3, 30, 600]]

------------------------------ Epoch 033 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.152721
- Iter 262 / 659, Loss: 0.082863
- Iter 393 / 659, Loss: 0.112476
- Iter 524 / 659, Loss: 0.029877
- Iter 655 / 659, Loss: 0.072100

* Train accuracy / confusion: 97.39% / [[8380, 117, 50], [162, 7229, 93], [57, 71, 4929]], 
* Val   accuracy / confusion: 93.83% / [[1060, 8, 1], [66, 870, 2], [55, 31, 547]]

------------------------------ Epoch 034 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.013165
- Iter 262 / 659, Loss: 0.152958
- Iter 393 / 659, Loss: 0.006565
- Iter 524 / 659, Loss: 0.212211
- Iter 655 / 659, Loss: 0.021280

* Train accuracy / confusion: 97.54% / [[8364, 129, 49], [170, 7266, 59], [56, 56, 4939]], 
* Val   accuracy / confusion: 96.55% / [[1040, 25, 4], [19, 909, 10], [20, 13, 600]]

------------------------------ Epoch 035 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.136871
- Iter 262 / 659, Loss: 0.178732
- Iter 393 / 659, Loss: 0.007329
- Iter 524 / 659, Loss: 0.117156
- Iter 655 / 659, Loss: 0.013741

* Train accuracy / confusion: 97.35% / [[8354, 124, 65], [154, 7257, 80], [62, 74, 4918]], 
* Val   accuracy / confusion: 94.39% / [[970, 83, 16], [18, 906, 14], [5, 12, 616]]

------------------------------ Epoch 036 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.003776
- Iter 262 / 659, Loss: 0.007009
- Iter 393 / 659, Loss: 0.006426
- Iter 524 / 659, Loss: 0.016547
- Iter 655 / 659, Loss: 0.016571

* Train accuracy / confusion: 97.63% / [[8374, 122, 49], [136, 7272, 79], [39, 74, 4943]], 
* Val   accuracy / confusion: 96.21% / [[1040, 21, 8], [26, 892, 20], [14, 11, 608]]

------------------------------ Epoch 037 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.009568
- Iter 262 / 659, Loss: 0.002453
- Iter 393 / 659, Loss: 0.100738
- Iter 524 / 659, Loss: 0.053634
- Iter 655 / 659, Loss: 0.067584

* Train accuracy / confusion: 97.90% / [[8393, 113, 41], [129, 7298, 62], [43, 54, 4955]], 
* Val   accuracy / confusion: 97.39% / [[1051, 17, 1], [18, 916, 4], [17, 12, 604]]

------------------------------ Epoch 038 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.020839
- Iter 262 / 659, Loss: 0.101889
- Iter 393 / 659, Loss: 0.007362
- Iter 524 / 659, Loss: 0.087253
- Iter 655 / 659, Loss: 0.007285

* Train accuracy / confusion: 97.91% / [[8393, 109, 42], [141, 7293, 53], [44, 52, 4961]], 
* Val   accuracy / confusion: 96.52% / [[1049, 15, 5], [39, 888, 11], [15, 7, 611]]

------------------------------ Epoch 039 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.130890
- Iter 262 / 659, Loss: 0.006868
- Iter 393 / 659, Loss: 0.067445
- Iter 524 / 659, Loss: 0.015907
- Iter 655 / 659, Loss: 0.132073

* Train accuracy / confusion: 97.74% / [[8388, 109, 49], [140, 7277, 67], [52, 59, 4947]], 
* Val   accuracy / confusion: 95.45% / [[1067, 2, 0], [64, 871, 3], [39, 12, 582]]

------------------------------ Epoch 040 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.104728
- Iter 262 / 659, Loss: 0.192060
- Iter 393 / 659, Loss: 0.043768
- Iter 524 / 659, Loss: 0.007796
- Iter 655 / 659, Loss: 0.039888

* Train accuracy / confusion: 98.13% / [[8396, 110, 38], [125, 7318, 45], [44, 32, 4980]], 
* Val   accuracy / confusion: 94.09% / [[984, 58, 27], [13, 916, 9], [5, 44, 584]]

------------------------------ Epoch 041 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.026368
- Iter 262 / 659, Loss: 0.053377
- Iter 393 / 659, Loss: 0.036952
- Iter 524 / 659, Loss: 0.037397
- Iter 655 / 659, Loss: 0.016401

* Train accuracy / confusion: 97.97% / [[8401, 104, 40], [126, 7306, 58], [43, 58, 4952]], 
* Val   accuracy / confusion: 95.72% / [[1061, 2, 6], [54, 858, 26], [13, 12, 608]]

------------------------------ Epoch 042 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.008259
- Iter 262 / 659, Loss: 0.046760
- Iter 393 / 659, Loss: 0.095125
- Iter 524 / 659, Loss: 0.090766
- Iter 655 / 659, Loss: 0.164622

* Train accuracy / confusion: 98.26% / [[8418, 89, 34], [109, 7340, 42], [43, 49, 4964]], 
* Val   accuracy / confusion: 95.27% / [[1061, 8, 0], [55, 882, 1], [26, 35, 572]]

------------------------------ Epoch 043 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.040042
- Iter 262 / 659, Loss: 0.000783
- Iter 393 / 659, Loss: 0.078005
- Iter 524 / 659, Loss: 0.147787
- Iter 655 / 659, Loss: 0.033123

* Train accuracy / confusion: 98.16% / [[8408, 95, 43], [116, 7335, 40], [40, 53, 4958]], 
* Val   accuracy / confusion: 96.82% / [[1028, 32, 9], [7, 915, 16], [6, 14, 613]]

------------------------------ Epoch 044 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.078721
- Iter 262 / 659, Loss: 0.061570
- Iter 393 / 659, Loss: 0.016789
- Iter 524 / 659, Loss: 0.003729
- Iter 655 / 659, Loss: 0.073184

* Train accuracy / confusion: 98.28% / [[8414, 93, 33], [110, 7331, 52], [35, 40, 4980]], 
* Val   accuracy / confusion: 95.87% / [[1055, 12, 2], [65, 865, 8], [14, 8, 611]]

------------------------------ Epoch 045 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.020696
- Iter 262 / 659, Loss: 0.004278
- Iter 393 / 659, Loss: 0.008459
- Iter 524 / 659, Loss: 0.074170
- Iter 655 / 659, Loss: 0.005359

* Train accuracy / confusion: 98.22% / [[8408, 100, 33], [118, 7324, 48], [32, 45, 4980]], 
* Val   accuracy / confusion: 98.26% / [[1048, 11, 10], [11, 922, 5], [7, 2, 624]]

------------------------------ Epoch 046 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.008676
- Iter 262 / 659, Loss: 0.082176
- Iter 393 / 659, Loss: 0.175999
- Iter 524 / 659, Loss: 0.024720
- Iter 655 / 659, Loss: 0.027604

* Train accuracy / confusion: 98.51% / [[8428, 91, 25], [105, 7353, 31], [25, 38, 4992]], 
* Val   accuracy / confusion: 96.93% / [[1053, 13, 3], [36, 898, 4], [9, 16, 608]]

------------------------------ Epoch 047 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.005906
- Iter 262 / 659, Loss: 0.028570
- Iter 393 / 659, Loss: 0.003742
- Iter 524 / 659, Loss: 0.114100
- Iter 655 / 659, Loss: 0.012265

* Train accuracy / confusion: 98.35% / [[8424, 86, 32], [89, 7354, 49], [42, 50, 4962]], 
* Val   accuracy / confusion: 97.27% / [[1062, 6, 1], [29, 906, 3], [21, 12, 600]]

------------------------------ Epoch 048 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.132535
- Iter 262 / 659, Loss: 0.081536
- Iter 393 / 659, Loss: 0.003935
- Iter 524 / 659, Loss: 0.034947
- Iter 655 / 659, Loss: 0.051564

* Train accuracy / confusion: 98.57% / [[8432, 89, 22], [92, 7360, 40], [29, 30, 4994]], 
* Val   accuracy / confusion: 97.69% / [[1063, 5, 1], [22, 906, 10], [14, 9, 610]]

------------------------------ Epoch 049 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.002366
- Iter 262 / 659, Loss: 0.081015
- Iter 393 / 659, Loss: 0.053538
- Iter 524 / 659, Loss: 0.002014
- Iter 655 / 659, Loss: 0.205106

* Train accuracy / confusion: 98.47% / [[8442, 61, 46], [97, 7354, 37], [47, 35, 4969]], 
* Val   accuracy / confusion: 97.84% / [[1052, 13, 4], [15, 919, 4], [7, 14, 612]]

------------------------------ Epoch 050 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.152700
- Iter 262 / 659, Loss: 0.021834
- Iter 393 / 659, Loss: 0.005743
- Iter 524 / 659, Loss: 0.005301
- Iter 655 / 659, Loss: 0.062263

* Train accuracy / confusion: 98.60% / [[8449, 63, 30], [75, 7366, 49], [26, 52, 4978]], 
* Val   accuracy / confusion: 96.21% / [[1062, 7, 0], [54, 880, 4], [29, 6, 598]]

------------------------------ Epoch 051 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.079635
- Iter 262 / 659, Loss: 0.008043
- Iter 393 / 659, Loss: 0.005145
- Iter 524 / 659, Loss: 0.023694
- Iter 655 / 659, Loss: 0.028589

* Train accuracy / confusion: 98.71% / [[8440, 72, 30], [80, 7381, 31], [27, 32, 4995]], 
* Val   accuracy / confusion: 97.80% / [[1045, 13, 11], [14, 916, 8], [6, 6, 621]]

------------------------------ Epoch 052 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.012397
- Iter 262 / 659, Loss: 0.005309
- Iter 393 / 659, Loss: 0.002827
- Iter 524 / 659, Loss: 0.038361
- Iter 655 / 659, Loss: 0.003970

* Train accuracy / confusion: 98.64% / [[8433, 81, 27], [92, 7362, 35], [24, 28, 5006]], 
* Val   accuracy / confusion: 98.07% / [[1057, 5, 7], [21, 908, 9], [5, 4, 624]]

------------------------------ Epoch 053 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.030443
- Iter 262 / 659, Loss: 0.066743
- Iter 393 / 659, Loss: 0.049904
- Iter 524 / 659, Loss: 0.028122
- Iter 655 / 659, Loss: 0.002620

* Train accuracy / confusion: 98.64% / [[8439, 62, 38], [87, 7378, 29], [36, 34, 4985]], 
* Val   accuracy / confusion: 98.14% / [[1058, 8, 3], [20, 914, 4], [5, 9, 619]]

------------------------------ Epoch 054 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.050096
- Iter 262 / 659, Loss: 0.006329
- Iter 393 / 659, Loss: 0.001184
- Iter 524 / 659, Loss: 0.079850
- Iter 655 / 659, Loss: 0.080198

* Train accuracy / confusion: 98.75% / [[8455, 60, 26], [73, 7373, 47], [24, 33, 4997]], 
* Val   accuracy / confusion: 93.75% / [[946, 108, 15], [2, 933, 3], [5, 32, 596]]

------------------------------ Epoch 055 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.037081
- Iter 262 / 659, Loss: 0.022865
- Iter 393 / 659, Loss: 0.001773
- Iter 524 / 659, Loss: 0.012630
- Iter 655 / 659, Loss: 0.082771

* Train accuracy / confusion: 98.87% / [[8457, 68, 20], [69, 7392, 28], [25, 29, 5000]], 
* Val   accuracy / confusion: 98.07% / [[1036, 23, 10], [8, 926, 4], [2, 4, 627]]

------------------------------ Epoch 056 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.001245
- Iter 262 / 659, Loss: 0.013396
- Iter 393 / 659, Loss: 0.178511
- Iter 524 / 659, Loss: 0.068934
- Iter 655 / 659, Loss: 0.045249

* Train accuracy / confusion: 98.79% / [[8449, 64, 31], [74, 7388, 27], [31, 29, 4995]], 
* Val   accuracy / confusion: 96.89% / [[1056, 4, 9], [40, 896, 2], [11, 16, 606]]

------------------------------ Epoch 057 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.199405
- Iter 262 / 659, Loss: 0.021959
- Iter 393 / 659, Loss: 0.016827
- Iter 524 / 659, Loss: 0.022070
- Iter 655 / 659, Loss: 0.009609

* Train accuracy / confusion: 98.91% / [[8468, 52, 26], [61, 7392, 35], [29, 27, 4998]], 
* Val   accuracy / confusion: 98.26% / [[1059, 6, 4], [18, 916, 4], [7, 7, 619]]

------------------------------ Epoch 058 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.005255
- Iter 262 / 659, Loss: 0.053947
- Iter 393 / 659, Loss: 0.004697
- Iter 524 / 659, Loss: 0.014277
- Iter 655 / 659, Loss: 0.014572

* Train accuracy / confusion: 98.90% / [[8469, 54, 24], [59, 7393, 39], [24, 31, 4995]], 
* Val   accuracy / confusion: 96.93% / [[1052, 12, 5], [43, 882, 13], [4, 4, 625]]

------------------------------ Epoch 059 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.007566
- Iter 262 / 659, Loss: 0.011395
- Iter 393 / 659, Loss: 0.021665
- Iter 524 / 659, Loss: 0.001334
- Iter 655 / 659, Loss: 0.024794

* Train accuracy / confusion: 98.70% / [[8437, 79, 27], [82, 7377, 31], [27, 28, 5000]], 
* Val   accuracy / confusion: 91.48% / [[875, 118, 76], [5, 916, 17], [1, 8, 624]]

------------------------------ Epoch 060 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.064104
- Iter 262 / 659, Loss: 0.001093
- Iter 393 / 659, Loss: 0.190301
- Iter 524 / 659, Loss: 0.277362
- Iter 655 / 659, Loss: 0.018225

* Train accuracy / confusion: 98.91% / [[8463, 52, 26], [69, 7397, 24], [31, 27, 4999]], 
* Val   accuracy / confusion: 97.54% / [[1058, 10, 1], [21, 912, 5], [13, 15, 605]]

------------------------------ Epoch 061 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.009361
- Iter 262 / 659, Loss: 0.003494
- Iter 393 / 659, Loss: 0.006922
- Iter 524 / 659, Loss: 0.005298
- Iter 655 / 659, Loss: 0.268059

* Train accuracy / confusion: 98.89% / [[8465, 59, 21], [73, 7387, 27], [22, 33, 5001]], 
* Val   accuracy / confusion: 97.27% / [[1047, 4, 18], [27, 892, 19], [2, 2, 629]]

------------------------------ Epoch 062 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.027355
- Iter 262 / 659, Loss: 0.037148
- Iter 393 / 659, Loss: 0.067929
- Iter 524 / 659, Loss: 0.006722
- Iter 655 / 659, Loss: 0.021486

* Train accuracy / confusion: 98.86% / [[8464, 53, 21], [65, 7392, 36], [25, 41, 4991]], 
* Val   accuracy / confusion: 97.92% / [[1047, 17, 5], [19, 913, 6], [3, 5, 625]]

------------------------------ Epoch 063 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.032274
- Iter 262 / 659, Loss: 0.012400
- Iter 393 / 659, Loss: 0.004293
- Iter 524 / 659, Loss: 0.006002
- Iter 655 / 659, Loss: 0.000570

* Train accuracy / confusion: 99.03% / [[8471, 49, 21], [57, 7401, 31], [19, 28, 5011]], 
* Val   accuracy / confusion: 97.95% / [[1052, 13, 4], [14, 920, 4], [7, 12, 614]]

------------------------------ Epoch 064 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.004617
- Iter 262 / 659, Loss: 0.082313
- Iter 393 / 659, Loss: 0.001193
- Iter 524 / 659, Loss: 0.000541
- Iter 655 / 659, Loss: 0.001241

* Train accuracy / confusion: 98.89% / [[8455, 65, 20], [66, 7396, 30], [23, 31, 5002]], 
* Val   accuracy / confusion: 94.96% / [[1062, 7, 0], [58, 878, 2], [21, 45, 567]]

------------------------------ Epoch 065 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.028419
- Iter 262 / 659, Loss: 0.027672
- Iter 393 / 659, Loss: 0.004290
- Iter 524 / 659, Loss: 0.039769
- Iter 655 / 659, Loss: 0.001555

* Train accuracy / confusion: 99.00% / [[8465, 51, 25], [68, 7400, 22], [26, 18, 5013]], 
* Val   accuracy / confusion: 97.31% / [[1037, 23, 9], [10, 913, 15], [8, 6, 619]]

------------------------------ Epoch 066 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.002673
- Iter 262 / 659, Loss: 0.195873
- Iter 393 / 659, Loss: 0.015917
- Iter 524 / 659, Loss: 0.000922
- Iter 655 / 659, Loss: 0.003838

* Train accuracy / confusion: 98.94% / [[8456, 59, 26], [62, 7405, 25], [29, 23, 5003]], 
* Val   accuracy / confusion: 97.88% / [[1036, 23, 10], [11, 919, 8], [2, 2, 629]]

------------------------------ Epoch 067 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.002630
- Iter 262 / 659, Loss: 0.000528
- Iter 393 / 659, Loss: 0.017185
- Iter 524 / 659, Loss: 0.002971
- Iter 655 / 659, Loss: 0.007504

* Train accuracy / confusion: 99.17% / [[8478, 49, 15], [53, 7419, 21], [19, 17, 5017]], 
* Val   accuracy / confusion: 97.42% / [[1061, 6, 2], [36, 898, 4], [15, 5, 613]]

------------------------------ Epoch 068 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.011809
- Iter 262 / 659, Loss: 0.018714
- Iter 393 / 659, Loss: 0.028540
- Iter 524 / 659, Loss: 0.001492
- Iter 655 / 659, Loss: 0.000893

* Train accuracy / confusion: 99.03% / [[8472, 50, 23], [61, 7397, 30], [17, 23, 5015]], 
* Val   accuracy / confusion: 96.86% / [[1020, 43, 6], [13, 922, 3], [5, 13, 615]]

------------------------------ Epoch 069 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.001020
- Iter 262 / 659, Loss: 0.000756
- Iter 393 / 659, Loss: 0.013900
- Iter 524 / 659, Loss: 0.000484
- Iter 655 / 659, Loss: 0.000842

* Train accuracy / confusion: 98.95% / [[8465, 58, 17], [58, 7400, 35], [20, 33, 5002]], 
* Val   accuracy / confusion: 98.33% / [[1054, 8, 7], [18, 916, 4], [3, 4, 626]]

------------------------------ Epoch 070 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.009085
- Iter 262 / 659, Loss: 0.049724
- Iter 393 / 659, Loss: 0.005211
- Iter 524 / 659, Loss: 0.016133
- Iter 655 / 659, Loss: 0.015458

* Train accuracy / confusion: 99.09% / [[8481, 46, 17], [59, 7398, 31], [15, 23, 5018]], 
* Val   accuracy / confusion: 96.97% / [[1036, 13, 20], [16, 895, 27], [2, 2, 629]]

------------------------------ Epoch 071 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.005240
- Iter 262 / 659, Loss: 0.031876
- Iter 393 / 659, Loss: 0.002834
- Iter 524 / 659, Loss: 0.017292
- Iter 655 / 659, Loss: 0.001477

* Train accuracy / confusion: 99.20% / [[8481, 45, 17], [46, 7416, 27], [15, 19, 5022]], 
* Val   accuracy / confusion: 98.52% / [[1062, 6, 1], [15, 918, 5], [7, 5, 621]]

------------------------------ Epoch 072 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.001283
- Iter 262 / 659, Loss: 0.000431
- Iter 393 / 659, Loss: 0.000576
- Iter 524 / 659, Loss: 0.005955
- Iter 655 / 659, Loss: 0.002511

* Train accuracy / confusion: 99.23% / [[8474, 45, 21], [41, 7430, 21], [17, 17, 5022]], 
* Val   accuracy / confusion: 97.99% / [[1063, 3, 3], [31, 902, 5], [5, 6, 622]]

------------------------------ Epoch 073 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.000860
- Iter 262 / 659, Loss: 0.001674
- Iter 393 / 659, Loss: 0.010235
- Iter 524 / 659, Loss: 0.069718
- Iter 655 / 659, Loss: 0.025902

* Train accuracy / confusion: 98.99% / [[8462, 63, 20], [61, 7401, 25], [18, 25, 5013]], 
* Val   accuracy / confusion: 96.97% / [[1051, 15, 3], [11, 926, 1], [9, 41, 583]]

------------------------------ Epoch 074 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.015732
- Iter 262 / 659, Loss: 0.000247
- Iter 393 / 659, Loss: 0.109871
- Iter 524 / 659, Loss: 0.001576
- Iter 655 / 659, Loss: 0.021773

* Train accuracy / confusion: 99.26% / [[8493, 41, 11], [47, 7415, 24], [14, 20, 5023]], 
* Val   accuracy / confusion: 96.06% / [[1043, 23, 3], [18, 918, 2], [13, 45, 575]]

------------------------------ Epoch 075 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.027196
- Iter 262 / 659, Loss: 0.003779
- Iter 393 / 659, Loss: 0.007307
- Iter 524 / 659, Loss: 0.000930
- Iter 655 / 659, Loss: 0.043568

* Train accuracy / confusion: 99.11% / [[8487, 38, 19], [46, 7413, 29], [26, 30, 5000]], 
* Val   accuracy / confusion: 98.14% / [[1052, 17, 0], [8, 929, 1], [11, 12, 610]]

------------------------------ Epoch 076 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.207224
- Iter 262 / 659, Loss: 0.001348
- Iter 393 / 659, Loss: 0.075469
- Iter 524 / 659, Loss: 0.000674
- Iter 655 / 659, Loss: 0.136883

* Train accuracy / confusion: 99.21% / [[8483, 43, 15], [42, 7422, 24], [18, 24, 5017]], 
* Val   accuracy / confusion: 95.80% / [[975, 35, 59], [4, 927, 7], [0, 6, 627]]

------------------------------ Epoch 077 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.092198
- Iter 262 / 659, Loss: 0.101486
- Iter 393 / 659, Loss: 0.018911
- Iter 524 / 659, Loss: 0.281405
- Iter 655 / 659, Loss: 0.002660

* Train accuracy / confusion: 99.17% / [[8471, 51, 21], [51, 7429, 10], [23, 19, 5013]], 
* Val   accuracy / confusion: 97.23% / [[1058, 11, 0], [20, 918, 0], [21, 21, 591]]

------------------------------ Epoch 078 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.003475
- Iter 262 / 659, Loss: 0.039850
- Iter 393 / 659, Loss: 0.048393
- Iter 524 / 659, Loss: 0.182851
- Iter 655 / 659, Loss: 0.012710

* Train accuracy / confusion: 99.24% / [[8487, 35, 20], [44, 7430, 16], [25, 20, 5011]], 
* Val   accuracy / confusion: 98.33% / [[1059, 8, 2], [16, 919, 3], [4, 11, 618]]

------------------------------ Epoch 079 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.004713
- Iter 262 / 659, Loss: 0.000560
- Iter 393 / 659, Loss: 0.128208
- Iter 524 / 659, Loss: 0.013945
- Iter 655 / 659, Loss: 0.044841

* Train accuracy / confusion: 99.27% / [[8486, 39, 18], [47, 7425, 16], [16, 17, 5024]], 
* Val   accuracy / confusion: 98.07% / [[1041, 17, 11], [13, 921, 4], [4, 2, 627]]

------------------------------ Epoch 080 / 200, Learning rate: 5.01e-03 ------------------------------
- Iter 131 / 659, Loss: 0.002949
- Iter 262 / 659, Loss: 0.003428
- Iter 393 / 659, Loss: 0.027390
- Iter 524 / 659, Loss: 0.048208
- Iter 655 / 659, Loss: 0.006303

* Train accuracy / confusion: 99.33% / [[8491, 32, 19], [44, 7431, 15], [16, 15, 5025]], 
* Val   accuracy / confusion: 98.14% / [[1042, 21, 6], [10, 924, 4], [3, 5, 625]]

------------------------------ Epoch 081 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000205
- Iter 262 / 659, Loss: 0.000488
- Iter 393 / 659, Loss: 0.024661
- Iter 524 / 659, Loss: 0.000096
- Iter 655 / 659, Loss: 0.001901

* Train accuracy / confusion: 99.73% / [[8529, 11, 4], [17, 7462, 10], [9, 6, 5040]], 
* Val   accuracy / confusion: 99.02% / [[1062, 7, 0], [8, 927, 3], [3, 5, 625]]

------------------------------ Epoch 082 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.004994
- Iter 262 / 659, Loss: 0.007939
- Iter 393 / 659, Loss: 0.001155
- Iter 524 / 659, Loss: 0.000118
- Iter 655 / 659, Loss: 0.004549

* Train accuracy / confusion: 99.87% / [[8531, 8, 5], [7, 7479, 1], [4, 2, 5051]], 
* Val   accuracy / confusion: 98.94% / [[1063, 4, 2], [11, 922, 5], [3, 3, 627]]

------------------------------ Epoch 083 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000179
- Iter 262 / 659, Loss: 0.000375
- Iter 393 / 659, Loss: 0.002799
- Iter 524 / 659, Loss: 0.000911
- Iter 655 / 659, Loss: 0.000119

* Train accuracy / confusion: 99.89% / [[8531, 8, 2], [9, 7480, 0], [4, 1, 5053]], 
* Val   accuracy / confusion: 98.94% / [[1063, 6, 0], [14, 921, 3], [4, 1, 628]]

------------------------------ Epoch 084 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000113
- Iter 262 / 659, Loss: 0.013106
- Iter 393 / 659, Loss: 0.000030
- Iter 524 / 659, Loss: 0.000038
- Iter 655 / 659, Loss: 0.000411

* Train accuracy / confusion: 99.89% / [[8533, 8, 3], [7, 7481, 3], [1, 1, 5051]], 
* Val   accuracy / confusion: 98.94% / [[1065, 4, 0], [15, 919, 4], [4, 1, 628]]

------------------------------ Epoch 085 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000061
- Iter 262 / 659, Loss: 0.000038
- Iter 393 / 659, Loss: 0.000142
- Iter 524 / 659, Loss: 0.000254
- Iter 655 / 659, Loss: 0.000419

* Train accuracy / confusion: 99.94% / [[8533, 4, 1], [4, 7488, 1], [1, 2, 5054]], 
* Val   accuracy / confusion: 99.17% / [[1064, 4, 1], [7, 927, 4], [1, 5, 627]]

------------------------------ Epoch 086 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000221
- Iter 262 / 659, Loss: 0.000318
- Iter 393 / 659, Loss: 0.000022
- Iter 524 / 659, Loss: 0.022708
- Iter 655 / 659, Loss: 0.001057

* Train accuracy / confusion: 99.92% / [[8538, 4, 2], [4, 7483, 2], [2, 2, 5051]], 
* Val   accuracy / confusion: 98.67% / [[1061, 7, 1], [11, 921, 6], [5, 5, 623]]

------------------------------ Epoch 087 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000017
- Iter 262 / 659, Loss: 0.001116
- Iter 393 / 659, Loss: 0.000328
- Iter 524 / 659, Loss: 0.014560
- Iter 655 / 659, Loss: 0.001944

* Train accuracy / confusion: 99.87% / [[8534, 8, 3], [4, 7480, 4], [5, 3, 5047]], 
* Val   accuracy / confusion: 98.98% / [[1065, 4, 0], [14, 920, 4], [4, 1, 628]]

------------------------------ Epoch 088 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.001283
- Iter 262 / 659, Loss: 0.000633
- Iter 393 / 659, Loss: 0.000087
- Iter 524 / 659, Loss: 0.000172
- Iter 655 / 659, Loss: 0.000010

* Train accuracy / confusion: 99.90% / [[8530, 9, 1], [7, 7485, 2], [3, 0, 5051]], 
* Val   accuracy / confusion: 99.05% / [[1063, 5, 1], [11, 926, 1], [1, 6, 626]]

------------------------------ Epoch 089 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.001025
- Iter 262 / 659, Loss: 0.000084
- Iter 393 / 659, Loss: 0.000099
- Iter 524 / 659, Loss: 0.000031
- Iter 655 / 659, Loss: 0.005502

* Train accuracy / confusion: 99.91% / [[8535, 4, 5], [4, 7489, 0], [4, 1, 5046]], 
* Val   accuracy / confusion: 99.02% / [[1066, 3, 0], [15, 921, 2], [2, 4, 627]]

------------------------------ Epoch 090 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000053
- Iter 262 / 659, Loss: 0.000028
- Iter 393 / 659, Loss: 0.017328
- Iter 524 / 659, Loss: 0.000134
- Iter 655 / 659, Loss: 0.000708

* Train accuracy / confusion: 99.96% / [[8546, 1, 1], [5, 7479, 1], [0, 1, 5054]], 
* Val   accuracy / confusion: 99.20% / [[1064, 5, 0], [4, 931, 3], [4, 5, 624]]

------------------------------ Epoch 091 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000027
- Iter 262 / 659, Loss: 0.000036
- Iter 393 / 659, Loss: 0.001921
- Iter 524 / 659, Loss: 0.000027
- Iter 655 / 659, Loss: 0.000022

* Train accuracy / confusion: 99.95% / [[8538, 3, 1], [2, 7487, 3], [1, 1, 5052]], 
* Val   accuracy / confusion: 99.02% / [[1066, 3, 0], [10, 928, 0], [5, 8, 620]]

------------------------------ Epoch 092 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000129
- Iter 262 / 659, Loss: 0.000019
- Iter 393 / 659, Loss: 0.000138
- Iter 524 / 659, Loss: 0.000039
- Iter 655 / 659, Loss: 0.000153

* Train accuracy / confusion: 99.92% / [[8536, 4, 0], [5, 7482, 5], [1, 2, 5053]], 
* Val   accuracy / confusion: 99.13% / [[1067, 2, 0], [13, 924, 1], [2, 5, 626]]

------------------------------ Epoch 093 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000136
- Iter 262 / 659, Loss: 0.000044
- Iter 393 / 659, Loss: 0.000025
- Iter 524 / 659, Loss: 0.000029
- Iter 655 / 659, Loss: 0.000174

* Train accuracy / confusion: 99.93% / [[8539, 3, 1], [6, 7484, 1], [1, 3, 5050]], 
* Val   accuracy / confusion: 99.13% / [[1062, 6, 1], [11, 927, 0], [1, 4, 628]]

------------------------------ Epoch 094 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000130
- Iter 262 / 659, Loss: 0.001513
- Iter 393 / 659, Loss: 0.000044
- Iter 524 / 659, Loss: 0.000026
- Iter 655 / 659, Loss: 0.000020

* Train accuracy / confusion: 99.95% / [[8536, 5, 1], [4, 7489, 0], [0, 1, 5052]], 
* Val   accuracy / confusion: 99.05% / [[1062, 5, 2], [8, 927, 3], [2, 5, 626]]

------------------------------ Epoch 095 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000148
- Iter 262 / 659, Loss: 0.000111
- Iter 393 / 659, Loss: 0.000011
- Iter 524 / 659, Loss: 0.000002
- Iter 655 / 659, Loss: 0.000024

* Train accuracy / confusion: 99.95% / [[8538, 3, 1], [3, 7484, 3], [1, 0, 5055]], 
* Val   accuracy / confusion: 99.02% / [[1065, 4, 0], [12, 924, 2], [4, 4, 625]]

------------------------------ Epoch 096 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000157
- Iter 262 / 659, Loss: 0.000277
- Iter 393 / 659, Loss: 0.000036
- Iter 524 / 659, Loss: 0.000004
- Iter 655 / 659, Loss: 0.000024

* Train accuracy / confusion: 99.93% / [[8538, 2, 0], [5, 7484, 3], [1, 3, 5052]], 
* Val   accuracy / confusion: 99.09% / [[1063, 6, 0], [8, 927, 3], [3, 4, 626]]

------------------------------ Epoch 097 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000026
- Iter 262 / 659, Loss: 0.000009
- Iter 393 / 659, Loss: 0.000296
- Iter 524 / 659, Loss: 0.000061
- Iter 655 / 659, Loss: 0.000287

* Train accuracy / confusion: 99.97% / [[8542, 3, 0], [3, 7488, 0], [1, 0, 5051]], 
* Val   accuracy / confusion: 99.20% / [[1065, 3, 1], [13, 924, 1], [2, 1, 630]]

------------------------------ Epoch 098 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000085
- Iter 262 / 659, Loss: 0.000041
- Iter 393 / 659, Loss: 0.000298
- Iter 524 / 659, Loss: 0.000010
- Iter 655 / 659, Loss: 0.000003

* Train accuracy / confusion: 99.97% / [[8541, 3, 1], [1, 7492, 1], [0, 0, 5049]], 
* Val   accuracy / confusion: 99.24% / [[1066, 3, 0], [11, 925, 2], [3, 1, 629]]

------------------------------ Epoch 099 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000011
- Iter 262 / 659, Loss: 0.000071
- Iter 393 / 659, Loss: 0.000026
- Iter 524 / 659, Loss: 0.000002
- Iter 655 / 659, Loss: 0.000280

* Train accuracy / confusion: 99.96% / [[8542, 2, 0], [4, 7486, 1], [1, 0, 5052]], 
* Val   accuracy / confusion: 99.39% / [[1063, 5, 1], [4, 932, 2], [2, 2, 629]]

------------------------------ Epoch 100 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.086723
- Iter 262 / 659, Loss: 0.000020
- Iter 393 / 659, Loss: 0.000033
- Iter 524 / 659, Loss: 0.000033
- Iter 655 / 659, Loss: 0.000189

* Train accuracy / confusion: 99.93% / [[8541, 4, 2], [4, 7481, 2], [1, 2, 5051]], 
* Val   accuracy / confusion: 98.94% / [[1065, 4, 0], [14, 921, 3], [5, 2, 626]]

------------------------------ Epoch 101 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000298
- Iter 262 / 659, Loss: 0.000003
- Iter 393 / 659, Loss: 0.004417
- Iter 524 / 659, Loss: 0.000009
- Iter 655 / 659, Loss: 0.026632

* Train accuracy / confusion: 99.94% / [[8538, 6, 1], [5, 7482, 1], [0, 0, 5055]], 
* Val   accuracy / confusion: 99.02% / [[1065, 3, 1], [9, 926, 3], [4, 6, 623]]

------------------------------ Epoch 102 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000018
- Iter 262 / 659, Loss: 0.000045
- Iter 393 / 659, Loss: 0.000165
- Iter 524 / 659, Loss: 0.000060
- Iter 655 / 659, Loss: 0.000220

* Train accuracy / confusion: 99.94% / [[8535, 4, 1], [5, 7488, 1], [1, 1, 5052]], 
* Val   accuracy / confusion: 98.83% / [[1063, 5, 1], [12, 924, 2], [8, 3, 622]]

------------------------------ Epoch 103 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000021
- Iter 262 / 659, Loss: 0.000010
- Iter 393 / 659, Loss: 0.000009
- Iter 524 / 659, Loss: 0.000011
- Iter 655 / 659, Loss: 0.000023

* Train accuracy / confusion: 99.97% / [[8541, 1, 0], [3, 7488, 1], [0, 1, 5053]], 
* Val   accuracy / confusion: 99.13% / [[1063, 5, 1], [10, 926, 2], [4, 1, 628]]

------------------------------ Epoch 104 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000006
- Iter 262 / 659, Loss: 0.000031
- Iter 393 / 659, Loss: 0.000076
- Iter 524 / 659, Loss: 0.029138
- Iter 655 / 659, Loss: 0.000002

* Train accuracy / confusion: 99.95% / [[8536, 3, 2], [2, 7488, 2], [0, 1, 5054]], 
* Val   accuracy / confusion: 98.83% / [[1068, 1, 0], [18, 919, 1], [6, 5, 622]]

------------------------------ Epoch 105 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000005
- Iter 262 / 659, Loss: 0.000051
- Iter 393 / 659, Loss: 0.000004
- Iter 524 / 659, Loss: 0.000003
- Iter 655 / 659, Loss: 0.000003

* Train accuracy / confusion: 99.98% / [[8540, 0, 0], [1, 7489, 1], [1, 1, 5055]], 
* Val   accuracy / confusion: 98.90% / [[1058, 10, 1], [7, 928, 3], [1, 7, 625]]

------------------------------ Epoch 106 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000068
- Iter 262 / 659, Loss: 0.000349
- Iter 393 / 659, Loss: 0.000001
- Iter 524 / 659, Loss: 0.000250
- Iter 655 / 659, Loss: 0.000008

* Train accuracy / confusion: 99.96% / [[8538, 2, 3], [2, 7488, 0], [1, 1, 5053]], 
* Val   accuracy / confusion: 98.75% / [[1062, 4, 3], [13, 921, 4], [5, 4, 624]]

------------------------------ Epoch 107 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000015
- Iter 262 / 659, Loss: 0.000032
- Iter 393 / 659, Loss: 0.000001
- Iter 524 / 659, Loss: 0.000020
- Iter 655 / 659, Loss: 0.000006

* Train accuracy / confusion: 99.96% / [[8541, 2, 1], [2, 7490, 2], [0, 1, 5049]], 
* Val   accuracy / confusion: 99.51% / [[1067, 2, 0], [3, 934, 1], [5, 2, 626]]

------------------------------ Epoch 108 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000001
- Iter 262 / 659, Loss: 0.000033
- Iter 393 / 659, Loss: 0.000005
- Iter 524 / 659, Loss: 0.000004
- Iter 655 / 659, Loss: 0.000101

* Train accuracy / confusion: 99.95% / [[8537, 2, 2], [3, 7490, 0], [1, 3, 5050]], 
* Val   accuracy / confusion: 99.32% / [[1063, 5, 1], [6, 931, 1], [1, 4, 628]]

------------------------------ Epoch 109 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.001016
- Iter 262 / 659, Loss: 0.000052
- Iter 393 / 659, Loss: 0.000004
- Iter 524 / 659, Loss: 0.000107
- Iter 655 / 659, Loss: 0.000001

* Train accuracy / confusion: 99.98% / [[8544, 1, 0], [2, 7487, 1], [0, 0, 5053]], 
* Val   accuracy / confusion: 99.09% / [[1063, 4, 2], [7, 929, 2], [5, 4, 624]]

------------------------------ Epoch 110 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000000
- Iter 262 / 659, Loss: 0.000004
- Iter 393 / 659, Loss: 0.000081
- Iter 524 / 659, Loss: 0.000048
- Iter 655 / 659, Loss: 0.000013

* Train accuracy / confusion: 99.97% / [[8544, 3, 0], [1, 7488, 2], [0, 1, 5049]], 
* Val   accuracy / confusion: 99.13% / [[1066, 3, 0], [10, 925, 3], [3, 4, 626]]

------------------------------ Epoch 111 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000002
- Iter 262 / 659, Loss: 0.000012
- Iter 393 / 659, Loss: 0.000444
- Iter 524 / 659, Loss: 0.000000
- Iter 655 / 659, Loss: 0.000001

* Train accuracy / confusion: 99.96% / [[8542, 3, 0], [3, 7485, 1], [0, 1, 5053]], 
* Val   accuracy / confusion: 99.36% / [[1066, 3, 0], [5, 932, 1], [3, 5, 625]]

------------------------------ Epoch 112 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000042
- Iter 262 / 659, Loss: 0.000023
- Iter 393 / 659, Loss: 0.000037
- Iter 524 / 659, Loss: 0.000015
- Iter 655 / 659, Loss: 0.000066

* Train accuracy / confusion: 99.98% / [[8539, 2, 0], [1, 7490, 1], [0, 1, 5054]], 
* Val   accuracy / confusion: 98.86% / [[1066, 3, 0], [14, 920, 4], [5, 4, 624]]

------------------------------ Epoch 113 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000731
- Iter 262 / 659, Loss: 0.000003
- Iter 393 / 659, Loss: 0.000002
- Iter 524 / 659, Loss: 0.000009
- Iter 655 / 659, Loss: 0.000451

* Train accuracy / confusion: 99.95% / [[8535, 6, 0], [2, 7488, 1], [1, 0, 5055]], 
* Val   accuracy / confusion: 99.05% / [[1063, 4, 2], [11, 923, 4], [3, 1, 629]]

------------------------------ Epoch 114 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000032
- Iter 262 / 659, Loss: 0.000031
- Iter 393 / 659, Loss: 0.000098
- Iter 524 / 659, Loss: 0.000003
- Iter 655 / 659, Loss: 0.000018

* Train accuracy / confusion: 99.94% / [[8538, 5, 1], [5, 7488, 0], [1, 0, 5050]], 
* Val   accuracy / confusion: 98.94% / [[1066, 1, 2], [18, 919, 1], [3, 3, 627]]

------------------------------ Epoch 115 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000186
- Iter 262 / 659, Loss: 0.003852
- Iter 393 / 659, Loss: 0.000003
- Iter 524 / 659, Loss: 0.000006
- Iter 655 / 659, Loss: 0.000005

* Train accuracy / confusion: 99.94% / [[8536, 2, 1], [5, 7486, 2], [3, 0, 5053]], 
* Val   accuracy / confusion: 99.13% / [[1062, 6, 1], [5, 932, 1], [2, 8, 623]]

------------------------------ Epoch 116 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000015
- Iter 262 / 659, Loss: 0.000043
- Iter 393 / 659, Loss: 0.000005
- Iter 524 / 659, Loss: 0.000011
- Iter 655 / 659, Loss: 0.000004

* Train accuracy / confusion: 99.96% / [[8543, 2, 1], [1, 7490, 2], [1, 2, 5046]], 
* Val   accuracy / confusion: 99.28% / [[1066, 3, 0], [8, 928, 2], [4, 2, 627]]

------------------------------ Epoch 117 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000001
- Iter 262 / 659, Loss: 0.000147
- Iter 393 / 659, Loss: 0.000001
- Iter 524 / 659, Loss: 0.000001
- Iter 655 / 659, Loss: 0.000024

* Train accuracy / confusion: 99.98% / [[8546, 0, 0], [1, 7483, 2], [0, 2, 5054]], 
* Val   accuracy / confusion: 99.32% / [[1068, 1, 0], [8, 925, 5], [2, 2, 629]]

------------------------------ Epoch 118 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.003020
- Iter 262 / 659, Loss: 0.000005
- Iter 393 / 659, Loss: 0.000005
- Iter 524 / 659, Loss: 0.000003
- Iter 655 / 659, Loss: 0.000005

* Train accuracy / confusion: 99.97% / [[8540, 1, 0], [3, 7486, 2], [1, 0, 5055]], 
* Val   accuracy / confusion: 99.13% / [[1065, 3, 1], [13, 924, 1], [1, 4, 628]]

------------------------------ Epoch 119 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000008
- Iter 262 / 659, Loss: 0.000134
- Iter 393 / 659, Loss: 0.000008
- Iter 524 / 659, Loss: 0.000011
- Iter 655 / 659, Loss: 0.000003

* Train accuracy / confusion: 99.98% / [[8542, 1, 1], [1, 7489, 0], [1, 1, 5052]], 
* Val   accuracy / confusion: 99.47% / [[1066, 3, 0], [8, 929, 1], [2, 0, 631]]

------------------------------ Epoch 120 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000905
- Iter 262 / 659, Loss: 0.000005
- Iter 393 / 659, Loss: 0.000144
- Iter 524 / 659, Loss: 0.000494
- Iter 655 / 659, Loss: 0.000005

* Train accuracy / confusion: 99.99% / [[8542, 1, 0], [1, 7492, 0], [0, 0, 5052]], 
* Val   accuracy / confusion: 99.05% / [[1067, 2, 0], [14, 921, 3], [4, 2, 627]]

------------------------------ Epoch 121 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000011
- Iter 262 / 659, Loss: 0.008577
- Iter 393 / 659, Loss: 0.000003
- Iter 524 / 659, Loss: 0.000004
- Iter 655 / 659, Loss: 0.000000

* Train accuracy / confusion: 99.97% / [[8541, 1, 3], [1, 7489, 0], [2, 0, 5051]], 
* Val   accuracy / confusion: 99.32% / [[1064, 5, 0], [7, 928, 3], [2, 1, 630]]

------------------------------ Epoch 122 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000830
- Iter 262 / 659, Loss: 0.000001
- Iter 393 / 659, Loss: 0.020915
- Iter 524 / 659, Loss: 0.000050
- Iter 655 / 659, Loss: 0.000011

* Train accuracy / confusion: 99.99% / [[8544, 0, 0], [0, 7490, 1], [1, 0, 5052]], 
* Val   accuracy / confusion: 99.39% / [[1066, 3, 0], [10, 927, 1], [1, 1, 631]]

------------------------------ Epoch 123 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000002
- Iter 262 / 659, Loss: 0.000012
- Iter 393 / 659, Loss: 0.000004
- Iter 524 / 659, Loss: 0.000001
- Iter 655 / 659, Loss: 0.000002

* Train accuracy / confusion: 99.96% / [[8542, 2, 1], [2, 7486, 1], [1, 1, 5052]], 
* Val   accuracy / confusion: 98.86% / [[1064, 4, 1], [11, 923, 4], [6, 4, 623]]

------------------------------ Epoch 124 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000001
- Iter 262 / 659, Loss: 0.000013
- Iter 393 / 659, Loss: 0.000022
- Iter 524 / 659, Loss: 0.000011
- Iter 655 / 659, Loss: 0.000033

* Train accuracy / confusion: 99.96% / [[8546, 1, 2], [4, 7479, 0], [1, 0, 5055]], 
* Val   accuracy / confusion: 99.09% / [[1065, 4, 0], [9, 928, 1], [7, 3, 623]]

------------------------------ Epoch 125 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000022
- Iter 262 / 659, Loss: 0.000003
- Iter 393 / 659, Loss: 0.000005
- Iter 524 / 659, Loss: 0.000008
- Iter 655 / 659, Loss: 0.000008

* Train accuracy / confusion: 99.98% / [[8540, 1, 0], [2, 7489, 1], [0, 1, 5054]], 
* Val   accuracy / confusion: 99.13% / [[1064, 5, 0], [7, 926, 5], [3, 3, 627]]

------------------------------ Epoch 126 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000002
- Iter 262 / 659, Loss: 0.000015
- Iter 393 / 659, Loss: 0.000000
- Iter 524 / 659, Loss: 0.000013
- Iter 655 / 659, Loss: 0.000122

* Train accuracy / confusion: 99.96% / [[8538, 3, 1], [2, 7488, 0], [1, 1, 5054]], 
* Val   accuracy / confusion: 99.02% / [[1062, 7, 0], [9, 928, 1], [5, 4, 624]]

------------------------------ Epoch 127 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000001
- Iter 262 / 659, Loss: 0.000014
- Iter 393 / 659, Loss: 0.000011
- Iter 524 / 659, Loss: 0.000003
- Iter 655 / 659, Loss: 0.000060

* Train accuracy / confusion: 99.93% / [[8536, 4, 0], [3, 7482, 4], [0, 3, 5056]], 
* Val   accuracy / confusion: 99.17% / [[1066, 2, 1], [11, 924, 3], [2, 3, 628]]

------------------------------ Epoch 128 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000180
- Iter 262 / 659, Loss: 0.000006
- Iter 393 / 659, Loss: 0.000023
- Iter 524 / 659, Loss: 0.000001
- Iter 655 / 659, Loss: 0.000029

* Train accuracy / confusion: 99.98% / [[8544, 0, 1], [3, 7483, 0], [0, 1, 5056]], 
* Val   accuracy / confusion: 98.83% / [[1065, 3, 1], [10, 925, 3], [7, 7, 619]]

------------------------------ Epoch 129 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.001111
- Iter 262 / 659, Loss: 0.000001
- Iter 393 / 659, Loss: 0.000006
- Iter 524 / 659, Loss: 0.006632
- Iter 655 / 659, Loss: 0.000003

* Train accuracy / confusion: 99.97% / [[8544, 2, 0], [3, 7484, 1], [0, 1, 5053]], 
* Val   accuracy / confusion: 99.09% / [[1063, 6, 0], [11, 924, 3], [0, 4, 629]]

------------------------------ Epoch 130 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000001
- Iter 262 / 659, Loss: 0.000003
- Iter 393 / 659, Loss: 0.000003
- Iter 524 / 659, Loss: 0.000047
- Iter 655 / 659, Loss: 0.000023

* Train accuracy / confusion: 99.98% / [[8542, 2, 1], [1, 7484, 1], [0, 0, 5057]], 
* Val   accuracy / confusion: 99.17% / [[1068, 1, 0], [13, 924, 1], [1, 6, 626]]

------------------------------ Epoch 131 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000024
- Iter 262 / 659, Loss: 0.000798
- Iter 393 / 659, Loss: 0.000001
- Iter 524 / 659, Loss: 0.000015
- Iter 655 / 659, Loss: 0.000010

* Train accuracy / confusion: 99.96% / [[8537, 3, 3], [3, 7490, 0], [0, 0, 5052]], 
* Val   accuracy / confusion: 99.58% / [[1067, 1, 1], [5, 933, 0], [1, 3, 629]]

------------------------------ Epoch 132 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000002
- Iter 262 / 659, Loss: 0.000010
- Iter 393 / 659, Loss: 0.000664
- Iter 524 / 659, Loss: 0.000001
- Iter 655 / 659, Loss: 0.000016

* Train accuracy / confusion: 99.98% / [[8543, 0, 1], [1, 7491, 0], [3, 0, 5049]], 
* Val   accuracy / confusion: 98.98% / [[1065, 2, 2], [11, 923, 4], [7, 1, 625]]

------------------------------ Epoch 133 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000193
- Iter 262 / 659, Loss: 0.000001
- Iter 393 / 659, Loss: 0.000009
- Iter 524 / 659, Loss: 0.000008
- Iter 655 / 659, Loss: 0.000003

* Train accuracy / confusion: 99.97% / [[8542, 2, 1], [2, 7484, 0], [1, 1, 5055]], 
* Val   accuracy / confusion: 99.43% / [[1067, 1, 1], [5, 932, 1], [6, 1, 626]]

------------------------------ Epoch 134 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000008
- Iter 262 / 659, Loss: 0.000542
- Iter 393 / 659, Loss: 0.000000
- Iter 524 / 659, Loss: 0.000039
- Iter 655 / 659, Loss: 0.000005

* Train accuracy / confusion: 99.97% / [[8541, 2, 0], [3, 7489, 1], [0, 0, 5052]], 
* Val   accuracy / confusion: 99.05% / [[1064, 5, 0], [8, 929, 1], [7, 4, 622]]

------------------------------ Epoch 135 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000001
- Iter 262 / 659, Loss: 0.000006
- Iter 393 / 659, Loss: 0.000123
- Iter 524 / 659, Loss: 0.000013
- Iter 655 / 659, Loss: 0.000022

* Train accuracy / confusion: 99.99% / [[8543, 1, 0], [1, 7487, 1], [0, 0, 5055]], 
* Val   accuracy / confusion: 99.17% / [[1066, 3, 0], [11, 926, 1], [3, 4, 626]]

------------------------------ Epoch 136 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000000
- Iter 262 / 659, Loss: 0.000035
- Iter 393 / 659, Loss: 0.000009
- Iter 524 / 659, Loss: 0.000024
- Iter 655 / 659, Loss: 0.000002

* Train accuracy / confusion: 99.96% / [[8540, 1, 2], [1, 7490, 1], [2, 2, 5049]], 
* Val   accuracy / confusion: 99.05% / [[1058, 6, 5], [5, 929, 4], [3, 2, 628]]

------------------------------ Epoch 137 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000007
- Iter 262 / 659, Loss: 0.000000
- Iter 393 / 659, Loss: 0.001572
- Iter 524 / 659, Loss: 0.000042
- Iter 655 / 659, Loss: 0.000061

* Train accuracy / confusion: 99.97% / [[8539, 2, 1], [0, 7492, 1], [0, 2, 5051]], 
* Val   accuracy / confusion: 99.39% / [[1068, 1, 0], [8, 929, 1], [5, 1, 627]]

------------------------------ Epoch 138 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000006
- Iter 262 / 659, Loss: 0.000389
- Iter 393 / 659, Loss: 0.000007
- Iter 524 / 659, Loss: 0.000001
- Iter 655 / 659, Loss: 0.000006

* Train accuracy / confusion: 99.95% / [[8538, 1, 1], [2, 7488, 2], [3, 1, 5052]], 
* Val   accuracy / confusion: 99.43% / [[1065, 4, 0], [7, 931, 0], [2, 2, 629]]

------------------------------ Epoch 139 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000003
- Iter 262 / 659, Loss: 0.000005
- Iter 393 / 659, Loss: 0.000002
- Iter 524 / 659, Loss: 0.000081
- Iter 655 / 659, Loss: 0.000006

* Train accuracy / confusion: 99.98% / [[8537, 3, 0], [1, 7492, 0], [0, 0, 5055]], 
* Val   accuracy / confusion: 99.02% / [[1065, 4, 0], [13, 921, 4], [2, 3, 628]]

------------------------------ Epoch 140 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000029
- Iter 262 / 659, Loss: 0.002799
- Iter 393 / 659, Loss: 0.000161
- Iter 524 / 659, Loss: 0.000021
- Iter 655 / 659, Loss: 0.000017

* Train accuracy / confusion: 99.95% / [[8539, 2, 0], [4, 7488, 1], [0, 3, 5051]], 
* Val   accuracy / confusion: 99.13% / [[1061, 7, 1], [4, 929, 5], [3, 3, 627]]

------------------------------ Epoch 141 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000002
- Iter 262 / 659, Loss: 0.000284
- Iter 393 / 659, Loss: 0.000022
- Iter 524 / 659, Loss: 0.015019
- Iter 655 / 659, Loss: 0.000082

* Train accuracy / confusion: 99.98% / [[8544, 1, 0], [0, 7488, 1], [1, 1, 5052]], 
* Val   accuracy / confusion: 99.17% / [[1065, 4, 0], [10, 925, 3], [2, 3, 628]]

------------------------------ Epoch 142 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000078
- Iter 262 / 659, Loss: 0.000001
- Iter 393 / 659, Loss: 0.000008
- Iter 524 / 659, Loss: 0.000009
- Iter 655 / 659, Loss: 0.000020

* Train accuracy / confusion: 99.99% / [[8542, 0, 1], [0, 7492, 0], [0, 1, 5052]], 
* Val   accuracy / confusion: 98.94% / [[1059, 8, 2], [6, 929, 3], [2, 7, 624]]

------------------------------ Epoch 143 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000000
- Iter 262 / 659, Loss: 0.000008
- Iter 393 / 659, Loss: 0.000001
- Iter 524 / 659, Loss: 0.000001
- Iter 655 / 659, Loss: 0.000199

* Train accuracy / confusion: 99.97% / [[8541, 2, 1], [2, 7487, 0], [1, 0, 5054]], 
* Val   accuracy / confusion: 99.28% / [[1064, 5, 0], [8, 928, 2], [1, 3, 629]]

------------------------------ Epoch 144 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000006
- Iter 262 / 659, Loss: 0.000001
- Iter 393 / 659, Loss: 0.000005
- Iter 524 / 659, Loss: 0.000503
- Iter 655 / 659, Loss: 0.004709

* Train accuracy / confusion: 99.97% / [[8541, 4, 0], [3, 7487, 0], [0, 0, 5053]], 
* Val   accuracy / confusion: 99.09% / [[1058, 8, 3], [2, 934, 2], [4, 5, 624]]

------------------------------ Epoch 145 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000020
- Iter 262 / 659, Loss: 0.000001
- Iter 393 / 659, Loss: 0.000033
- Iter 524 / 659, Loss: 0.000017
- Iter 655 / 659, Loss: 0.000007

* Train accuracy / confusion: 99.96% / [[8540, 2, 1], [2, 7490, 2], [2, 0, 5049]], 
* Val   accuracy / confusion: 99.05% / [[1059, 10, 0], [5, 931, 2], [1, 7, 625]]

------------------------------ Epoch 146 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000033
- Iter 262 / 659, Loss: 0.000034
- Iter 393 / 659, Loss: 0.000063
- Iter 524 / 659, Loss: 0.000056
- Iter 655 / 659, Loss: 0.000004

* Train accuracy / confusion: 99.99% / [[8538, 1, 0], [2, 7489, 0], [0, 0, 5058]], 
* Val   accuracy / confusion: 99.13% / [[1064, 5, 0], [10, 926, 2], [3, 3, 627]]

------------------------------ Epoch 147 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000066
- Iter 262 / 659, Loss: 0.000005
- Iter 393 / 659, Loss: 0.000001
- Iter 524 / 659, Loss: 0.000079
- Iter 655 / 659, Loss: 0.000042

* Train accuracy / confusion: 99.98% / [[8543, 2, 0], [1, 7486, 1], [0, 0, 5055]], 
* Val   accuracy / confusion: 98.83% / [[1061, 8, 0], [13, 925, 0], [7, 3, 623]]

------------------------------ Epoch 148 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000000
- Iter 262 / 659, Loss: 0.000012
- Iter 393 / 659, Loss: 0.000004
- Iter 524 / 659, Loss: 0.001929
- Iter 655 / 659, Loss: 0.000020

* Train accuracy / confusion: 99.96% / [[8544, 2, 1], [3, 7488, 1], [1, 0, 5048]], 
* Val   accuracy / confusion: 99.24% / [[1064, 4, 1], [8, 929, 1], [0, 6, 627]]

------------------------------ Epoch 149 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000003
- Iter 262 / 659, Loss: 0.000001
- Iter 393 / 659, Loss: 0.000002
- Iter 524 / 659, Loss: 0.000021
- Iter 655 / 659, Loss: 0.000040

* Train accuracy / confusion: 99.98% / [[8540, 1, 1], [2, 7491, 0], [0, 0, 5053]], 
* Val   accuracy / confusion: 98.94% / [[1063, 6, 0], [10, 927, 1], [4, 7, 622]]

------------------------------ Epoch 150 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000023
- Iter 262 / 659, Loss: 0.000002
- Iter 393 / 659, Loss: 0.000003
- Iter 524 / 659, Loss: 0.033373
- Iter 655 / 659, Loss: 0.000006

* Train accuracy / confusion: 99.98% / [[8541, 1, 0], [2, 7490, 0], [1, 0, 5053]], 
* Val   accuracy / confusion: 99.20% / [[1067, 2, 0], [9, 925, 4], [3, 3, 627]]

------------------------------ Epoch 151 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000005
- Iter 262 / 659, Loss: 0.000001
- Iter 393 / 659, Loss: 0.000001
- Iter 524 / 659, Loss: 0.000004
- Iter 655 / 659, Loss: 0.000003

* Train accuracy / confusion: 99.97% / [[8537, 1, 0], [2, 7489, 2], [0, 1, 5056]], 
* Val   accuracy / confusion: 99.05% / [[1065, 2, 2], [9, 928, 1], [6, 5, 622]]

------------------------------ Epoch 152 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000011
- Iter 262 / 659, Loss: 0.000001
- Iter 393 / 659, Loss: 0.000059
- Iter 524 / 659, Loss: 0.000000
- Iter 655 / 659, Loss: 0.000000

* Train accuracy / confusion: 99.97% / [[8542, 1, 0], [4, 7489, 0], [0, 1, 5051]], 
* Val   accuracy / confusion: 98.94% / [[1061, 7, 1], [9, 928, 1], [5, 5, 623]]

------------------------------ Epoch 153 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000019
- Iter 262 / 659, Loss: 0.000463
- Iter 393 / 659, Loss: 0.000270
- Iter 524 / 659, Loss: 0.000000
- Iter 655 / 659, Loss: 0.000006

* Train accuracy / confusion: 99.96% / [[8538, 3, 0], [4, 7491, 0], [1, 0, 5051]], 
* Val   accuracy / confusion: 99.05% / [[1061, 8, 0], [9, 927, 2], [2, 4, 627]]

------------------------------ Epoch 154 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000009
- Iter 262 / 659, Loss: 0.000092
- Iter 393 / 659, Loss: 0.000026
- Iter 524 / 659, Loss: 0.000008
- Iter 655 / 659, Loss: 0.000035

* Train accuracy / confusion: 99.94% / [[8540, 4, 2], [3, 7486, 0], [1, 2, 5050]], 
* Val   accuracy / confusion: 99.20% / [[1067, 1, 1], [10, 926, 2], [4, 3, 626]]

------------------------------ Epoch 155 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.010567
- Iter 262 / 659, Loss: 0.000099
- Iter 393 / 659, Loss: 0.000003
- Iter 524 / 659, Loss: 0.000589
- Iter 655 / 659, Loss: 0.000010

* Train accuracy / confusion: 99.99% / [[8544, 0, 2], [1, 7490, 0], [0, 0, 5051]], 
* Val   accuracy / confusion: 99.02% / [[1066, 3, 0], [11, 927, 0], [6, 6, 621]]

------------------------------ Epoch 156 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000008
- Iter 262 / 659, Loss: 0.000001
- Iter 393 / 659, Loss: 0.000000
- Iter 524 / 659, Loss: 0.000006
- Iter 655 / 659, Loss: 0.000007

* Train accuracy / confusion: 99.99% / [[8538, 2, 0], [0, 7492, 0], [0, 0, 5056]], 
* Val   accuracy / confusion: 99.17% / [[1066, 3, 0], [13, 923, 2], [2, 2, 629]]

------------------------------ Epoch 157 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000002
- Iter 262 / 659, Loss: 0.002563
- Iter 393 / 659, Loss: 0.000021
- Iter 524 / 659, Loss: 0.000064
- Iter 655 / 659, Loss: 0.000006

* Train accuracy / confusion: 99.96% / [[8541, 2, 0], [3, 7485, 3], [0, 1, 5053]], 
* Val   accuracy / confusion: 99.17% / [[1064, 3, 2], [10, 927, 1], [3, 3, 627]]

------------------------------ Epoch 158 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000004
- Iter 262 / 659, Loss: 0.000001
- Iter 393 / 659, Loss: 0.000002
- Iter 524 / 659, Loss: 0.000000
- Iter 655 / 659, Loss: 0.000005

* Train accuracy / confusion: 99.99% / [[8544, 1, 0], [2, 7488, 0], [0, 0, 5053]], 
* Val   accuracy / confusion: 99.13% / [[1063, 4, 2], [9, 929, 0], [2, 6, 625]]

------------------------------ Epoch 159 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000001
- Iter 262 / 659, Loss: 0.000018
- Iter 393 / 659, Loss: 0.000003
- Iter 524 / 659, Loss: 0.000005
- Iter 655 / 659, Loss: 0.000003

* Train accuracy / confusion: 99.98% / [[8534, 2, 0], [2, 7495, 0], [1, 0, 5054]], 
* Val   accuracy / confusion: 99.05% / [[1064, 4, 1], [12, 923, 3], [1, 4, 628]]

------------------------------ Epoch 160 / 200, Learning rate: 5.01e-04 ------------------------------
- Iter 131 / 659, Loss: 0.000028
- Iter 262 / 659, Loss: 0.003383
- Iter 393 / 659, Loss: 0.000001
- Iter 524 / 659, Loss: 0.000016
- Iter 655 / 659, Loss: 0.000001

* Train accuracy / confusion: 99.96% / [[8538, 0, 3], [1, 7491, 1], [1, 2, 5051]], 
* Val   accuracy / confusion: 98.94% / [[1067, 2, 0], [15, 918, 5], [3, 3, 627]]

------------------------------ Epoch 161 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.050221
- Iter 262 / 659, Loss: 0.000007
- Iter 393 / 659, Loss: 0.000002
- Iter 524 / 659, Loss: 0.000002
- Iter 655 / 659, Loss: 0.000005

* Train accuracy / confusion: 99.98% / [[8549, 0, 0], [1, 7484, 3], [1, 0, 5050]], 
* Val   accuracy / confusion: 99.28% / [[1065, 4, 0], [9, 928, 1], [2, 3, 628]]

------------------------------ Epoch 162 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000007
- Iter 262 / 659, Loss: 0.000020
- Iter 393 / 659, Loss: 0.000010
- Iter 524 / 659, Loss: 0.000022
- Iter 655 / 659, Loss: 0.000013

* Train accuracy / confusion: 99.98% / [[8539, 2, 0], [0, 7491, 2], [0, 0, 5054]], 
* Val   accuracy / confusion: 99.20% / [[1067, 0, 2], [11, 925, 2], [3, 3, 627]]

------------------------------ Epoch 163 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000046
- Iter 262 / 659, Loss: 0.000001
- Iter 393 / 659, Loss: 0.000002
- Iter 524 / 659, Loss: 0.000006
- Iter 655 / 659, Loss: 0.000005

* Train accuracy / confusion: 99.98% / [[8536, 1, 1], [1, 7490, 0], [1, 0, 5058]], 
* Val   accuracy / confusion: 99.13% / [[1061, 7, 1], [8, 928, 2], [3, 2, 628]]

------------------------------ Epoch 164 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000001
- Iter 262 / 659, Loss: 0.000001
- Iter 393 / 659, Loss: 0.000000
- Iter 524 / 659, Loss: 0.000013
- Iter 655 / 659, Loss: 0.000002

* Train accuracy / confusion: 99.99% / [[8545, 0, 0], [2, 7488, 0], [0, 0, 5053]], 
* Val   accuracy / confusion: 99.20% / [[1063, 5, 1], [5, 931, 2], [1, 7, 625]]

------------------------------ Epoch 165 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000007
- Iter 262 / 659, Loss: 0.000007
- Iter 393 / 659, Loss: 0.000027
- Iter 524 / 659, Loss: 0.000003
- Iter 655 / 659, Loss: 0.000003

* Train accuracy / confusion: 100.00% / [[8539, 0, 0], [0, 7491, 0], [0, 0, 5058]], 
* Val   accuracy / confusion: 99.05% / [[1063, 4, 2], [7, 926, 5], [4, 3, 626]]

------------------------------ Epoch 166 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000002
- Iter 262 / 659, Loss: 0.000010
- Iter 393 / 659, Loss: 0.000003
- Iter 524 / 659, Loss: 0.000001
- Iter 655 / 659, Loss: 0.000002

* Train accuracy / confusion: 99.99% / [[8543, 1, 0], [1, 7485, 0], [0, 0, 5058]], 
* Val   accuracy / confusion: 99.20% / [[1064, 4, 1], [8, 927, 3], [3, 2, 628]]

------------------------------ Epoch 167 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000165
- Iter 262 / 659, Loss: 0.000075
- Iter 393 / 659, Loss: 0.000419
- Iter 524 / 659, Loss: 0.000006
- Iter 655 / 659, Loss: 0.000001

* Train accuracy / confusion: 99.97% / [[8549, 1, 0], [2, 7488, 1], [1, 1, 5045]], 
* Val   accuracy / confusion: 99.05% / [[1063, 5, 1], [7, 926, 5], [3, 4, 626]]

------------------------------ Epoch 168 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000051
- Iter 262 / 659, Loss: 0.000001
- Iter 393 / 659, Loss: 0.000010
- Iter 524 / 659, Loss: 0.000021
- Iter 655 / 659, Loss: 0.000017

* Train accuracy / confusion: 99.97% / [[8537, 2, 2], [2, 7489, 0], [0, 0, 5056]], 
* Val   accuracy / confusion: 99.20% / [[1066, 3, 0], [10, 926, 2], [3, 3, 627]]

------------------------------ Epoch 169 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000004
- Iter 262 / 659, Loss: 0.000001
- Iter 393 / 659, Loss: 0.000062
- Iter 524 / 659, Loss: 0.000006
- Iter 655 / 659, Loss: 0.000002

* Train accuracy / confusion: 99.98% / [[8538, 1, 0], [1, 7491, 0], [0, 2, 5055]], 
* Val   accuracy / confusion: 99.24% / [[1067, 1, 1], [9, 929, 0], [6, 3, 624]]

------------------------------ Epoch 170 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000001
- Iter 262 / 659, Loss: 0.000007
- Iter 393 / 659, Loss: 0.000001
- Iter 524 / 659, Loss: 0.000000
- Iter 655 / 659, Loss: 0.000001

* Train accuracy / confusion: 100.00% / [[8542, 0, 0], [0, 7492, 0], [0, 1, 5053]], 
* Val   accuracy / confusion: 99.32% / [[1067, 2, 0], [8, 928, 2], [4, 2, 627]]

------------------------------ Epoch 171 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000032
- Iter 262 / 659, Loss: 0.000065
- Iter 393 / 659, Loss: 0.000003
- Iter 524 / 659, Loss: 0.000000
- Iter 655 / 659, Loss: 0.000001

* Train accuracy / confusion: 100.00% / [[8545, 0, 0], [0, 7488, 0], [0, 0, 5055]], 
* Val   accuracy / confusion: 99.51% / [[1067, 2, 0], [5, 930, 3], [2, 1, 630]]

------------------------------ Epoch 172 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000002
- Iter 262 / 659, Loss: 0.000043
- Iter 393 / 659, Loss: 0.000023
- Iter 524 / 659, Loss: 0.000000
- Iter 655 / 659, Loss: 0.000005

* Train accuracy / confusion: 100.00% / [[8549, 0, 0], [0, 7485, 0], [0, 0, 5054]], 
* Val   accuracy / confusion: 99.28% / [[1063, 6, 0], [10, 928, 0], [1, 2, 630]]

------------------------------ Epoch 173 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000013
- Iter 262 / 659, Loss: 0.000014
- Iter 393 / 659, Loss: 0.000010
- Iter 524 / 659, Loss: 0.000010
- Iter 655 / 659, Loss: 0.000006

* Train accuracy / confusion: 100.00% / [[8538, 0, 0], [0, 7493, 0], [0, 0, 5057]], 
* Val   accuracy / confusion: 99.13% / [[1064, 3, 2], [9, 926, 3], [2, 4, 627]]

------------------------------ Epoch 174 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000005
- Iter 262 / 659, Loss: 0.000024
- Iter 393 / 659, Loss: 0.000008
- Iter 524 / 659, Loss: 0.001669
- Iter 655 / 659, Loss: 0.000020

* Train accuracy / confusion: 99.97% / [[8534, 2, 0], [3, 7492, 0], [1, 1, 5055]], 
* Val   accuracy / confusion: 99.32% / [[1065, 2, 2], [8, 927, 3], [1, 2, 630]]

------------------------------ Epoch 175 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000002
- Iter 262 / 659, Loss: 0.037828
- Iter 393 / 659, Loss: 0.000004
- Iter 524 / 659, Loss: 0.000001
- Iter 655 / 659, Loss: 0.000001

* Train accuracy / confusion: 99.99% / [[8541, 2, 0], [0, 7489, 0], [0, 1, 5055]], 
* Val   accuracy / confusion: 99.09% / [[1062, 6, 1], [8, 926, 4], [4, 1, 628]]

------------------------------ Epoch 176 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000002
- Iter 262 / 659, Loss: 0.000033
- Iter 393 / 659, Loss: 0.000062
- Iter 524 / 659, Loss: 0.000000
- Iter 655 / 659, Loss: 0.000179

* Train accuracy / confusion: 99.99% / [[8543, 1, 0], [0, 7487, 0], [1, 1, 5055]], 
* Val   accuracy / confusion: 99.17% / [[1063, 6, 0], [5, 928, 5], [4, 2, 627]]

------------------------------ Epoch 177 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000001
- Iter 262 / 659, Loss: 0.000001
- Iter 393 / 659, Loss: 0.000004
- Iter 524 / 659, Loss: 0.000000
- Iter 655 / 659, Loss: 0.000003

* Train accuracy / confusion: 99.99% / [[8543, 2, 1], [0, 7492, 0], [0, 0, 5050]], 
* Val   accuracy / confusion: 99.36% / [[1067, 2, 0], [9, 926, 3], [1, 2, 630]]

------------------------------ Epoch 178 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000009
- Iter 262 / 659, Loss: 0.000013
- Iter 393 / 659, Loss: 0.000005
- Iter 524 / 659, Loss: 0.000002
- Iter 655 / 659, Loss: 0.000001

* Train accuracy / confusion: 100.00% / [[8543, 0, 0], [0, 7490, 1], [0, 0, 5054]], 
* Val   accuracy / confusion: 99.05% / [[1064, 3, 2], [13, 923, 2], [3, 2, 628]]

------------------------------ Epoch 179 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000001
- Iter 262 / 659, Loss: 0.000116
- Iter 393 / 659, Loss: 0.000005
- Iter 524 / 659, Loss: 0.000008
- Iter 655 / 659, Loss: 0.000015

* Train accuracy / confusion: 99.99% / [[8545, 1, 0], [0, 7489, 1], [0, 0, 5052]], 
* Val   accuracy / confusion: 98.86% / [[1061, 4, 4], [14, 921, 3], [4, 1, 628]]

------------------------------ Epoch 180 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000039
- Iter 262 / 659, Loss: 0.000001
- Iter 393 / 659, Loss: 0.000346
- Iter 524 / 659, Loss: 0.000001
- Iter 655 / 659, Loss: 0.000117

* Train accuracy / confusion: 99.99% / [[8540, 0, 0], [2, 7492, 0], [0, 0, 5054]], 
* Val   accuracy / confusion: 99.20% / [[1067, 2, 0], [11, 927, 0], [4, 4, 625]]

------------------------------ Epoch 181 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000015
- Iter 262 / 659, Loss: 0.000000
- Iter 393 / 659, Loss: 0.000001
- Iter 524 / 659, Loss: 0.000007
- Iter 655 / 659, Loss: 0.000013

* Train accuracy / confusion: 99.99% / [[8540, 0, 0], [2, 7491, 0], [1, 0, 5054]], 
* Val   accuracy / confusion: 99.32% / [[1064, 3, 2], [6, 930, 2], [2, 3, 628]]

------------------------------ Epoch 182 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000002
- Iter 262 / 659, Loss: 0.000008
- Iter 393 / 659, Loss: 0.000195
- Iter 524 / 659, Loss: 0.000050
- Iter 655 / 659, Loss: 0.000033

* Train accuracy / confusion: 99.99% / [[8544, 1, 0], [1, 7490, 0], [0, 0, 5052]], 
* Val   accuracy / confusion: 99.09% / [[1066, 3, 0], [12, 924, 2], [5, 2, 626]]

------------------------------ Epoch 183 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000005
- Iter 262 / 659, Loss: 0.000043
- Iter 393 / 659, Loss: 0.000008
- Iter 524 / 659, Loss: 0.000001
- Iter 655 / 659, Loss: 0.000001

* Train accuracy / confusion: 100.00% / [[8541, 0, 0], [1, 7491, 0], [0, 0, 5055]], 
* Val   accuracy / confusion: 99.24% / [[1063, 5, 1], [8, 930, 0], [1, 5, 627]]

------------------------------ Epoch 184 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000003
- Iter 262 / 659, Loss: 0.000021
- Iter 393 / 659, Loss: 0.000001
- Iter 524 / 659, Loss: 0.000054
- Iter 655 / 659, Loss: 0.000011

* Train accuracy / confusion: 100.00% / [[8545, 0, 0], [0, 7487, 0], [0, 0, 5056]], 
* Val   accuracy / confusion: 99.17% / [[1063, 6, 0], [9, 928, 1], [3, 3, 627]]

------------------------------ Epoch 185 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000000
- Iter 262 / 659, Loss: 0.000001
- Iter 393 / 659, Loss: 0.000057
- Iter 524 / 659, Loss: 0.000002
- Iter 655 / 659, Loss: 0.000135

* Train accuracy / confusion: 100.00% / [[8542, 0, 0], [0, 7489, 0], [0, 0, 5057]], 
* Val   accuracy / confusion: 99.09% / [[1063, 6, 0], [7, 929, 2], [1, 8, 624]]

------------------------------ Epoch 186 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000002
- Iter 262 / 659, Loss: 0.000014
- Iter 393 / 659, Loss: 0.000004
- Iter 524 / 659, Loss: 0.000002
- Iter 655 / 659, Loss: 0.000003

* Train accuracy / confusion: 99.99% / [[8538, 1, 0], [0, 7492, 1], [0, 0, 5056]], 
* Val   accuracy / confusion: 99.17% / [[1065, 4, 0], [7, 927, 4], [4, 3, 626]]

------------------------------ Epoch 187 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000001
- Iter 262 / 659, Loss: 0.000000
- Iter 393 / 659, Loss: 0.000002
- Iter 524 / 659, Loss: 0.000145
- Iter 655 / 659, Loss: 0.000004

* Train accuracy / confusion: 100.00% / [[8543, 0, 0], [0, 7491, 0], [1, 0, 5053]], 
* Val   accuracy / confusion: 99.24% / [[1064, 4, 1], [6, 931, 1], [2, 6, 625]]

------------------------------ Epoch 188 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000151
- Iter 262 / 659, Loss: 0.000103
- Iter 393 / 659, Loss: 0.000001
- Iter 524 / 659, Loss: 0.000001
- Iter 655 / 659, Loss: 0.000000

* Train accuracy / confusion: 99.99% / [[8544, 0, 0], [1, 7486, 1], [0, 0, 5056]], 
* Val   accuracy / confusion: 99.36% / [[1064, 3, 2], [7, 931, 0], [3, 2, 628]]

------------------------------ Epoch 189 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000001
- Iter 262 / 659, Loss: 0.000000
- Iter 393 / 659, Loss: 0.000000
- Iter 524 / 659, Loss: 0.000000
- Iter 655 / 659, Loss: 0.000003

* Train accuracy / confusion: 99.99% / [[8544, 1, 0], [1, 7484, 0], [0, 0, 5058]], 
* Val   accuracy / confusion: 99.39% / [[1065, 2, 2], [7, 930, 1], [2, 2, 629]]

------------------------------ Epoch 190 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000004
- Iter 262 / 659, Loss: 0.000001
- Iter 393 / 659, Loss: 0.000001
- Iter 524 / 659, Loss: 0.000001
- Iter 655 / 659, Loss: 0.000002

* Train accuracy / confusion: 100.00% / [[8546, 0, 0], [0, 7486, 0], [0, 1, 5055]], 
* Val   accuracy / confusion: 99.20% / [[1067, 2, 0], [11, 925, 2], [5, 1, 627]]

------------------------------ Epoch 191 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000000
- Iter 262 / 659, Loss: 0.000197
- Iter 393 / 659, Loss: 0.000001
- Iter 524 / 659, Loss: 0.000032
- Iter 655 / 659, Loss: 0.000004

* Train accuracy / confusion: 100.00% / [[8542, 0, 0], [1, 7488, 0], [0, 0, 5057]], 
* Val   accuracy / confusion: 99.09% / [[1067, 1, 1], [13, 923, 2], [5, 2, 626]]

------------------------------ Epoch 192 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000001
- Iter 262 / 659, Loss: 0.000000
- Iter 393 / 659, Loss: 0.000001
- Iter 524 / 659, Loss: 0.000000
- Iter 655 / 659, Loss: 0.000002

* Train accuracy / confusion: 99.99% / [[8537, 1, 0], [0, 7494, 0], [1, 0, 5055]], 
* Val   accuracy / confusion: 99.28% / [[1065, 3, 1], [12, 925, 1], [1, 1, 631]]

------------------------------ Epoch 193 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000000
- Iter 262 / 659, Loss: 0.000015
- Iter 393 / 659, Loss: 0.000252
- Iter 524 / 659, Loss: 0.000011
- Iter 655 / 659, Loss: 0.000007

* Train accuracy / confusion: 99.99% / [[8544, 0, 1], [1, 7487, 0], [1, 0, 5054]], 
* Val   accuracy / confusion: 99.28% / [[1063, 4, 2], [7, 930, 1], [3, 2, 628]]

------------------------------ Epoch 194 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000004
- Iter 262 / 659, Loss: 0.000012
- Iter 393 / 659, Loss: 0.010625
- Iter 524 / 659, Loss: 0.000121
- Iter 655 / 659, Loss: 0.000012

* Train accuracy / confusion: 99.98% / [[8542, 1, 2], [0, 7493, 0], [0, 1, 5049]], 
* Val   accuracy / confusion: 99.24% / [[1064, 3, 2], [7, 930, 1], [3, 4, 626]]

------------------------------ Epoch 195 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000000
- Iter 262 / 659, Loss: 0.000002
- Iter 393 / 659, Loss: 0.000003
- Iter 524 / 659, Loss: 0.000003
- Iter 655 / 659, Loss: 0.000031

* Train accuracy / confusion: 100.00% / [[8545, 0, 0], [0, 7493, 0], [0, 0, 5050]], 
* Val   accuracy / confusion: 99.32% / [[1066, 3, 0], [11, 925, 2], [0, 2, 631]]

------------------------------ Epoch 196 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000000
- Iter 262 / 659, Loss: 0.000000
- Iter 393 / 659, Loss: 0.000000
- Iter 524 / 659, Loss: 0.000001
- Iter 655 / 659, Loss: 0.000003

* Train accuracy / confusion: 99.99% / [[8541, 1, 1], [1, 7487, 0], [0, 0, 5057]], 
* Val   accuracy / confusion: 99.13% / [[1064, 3, 2], [6, 931, 1], [6, 5, 622]]

------------------------------ Epoch 197 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.001361
- Iter 262 / 659, Loss: 0.000003
- Iter 393 / 659, Loss: 0.000001
- Iter 524 / 659, Loss: 0.000000
- Iter 655 / 659, Loss: 0.000001

* Train accuracy / confusion: 99.98% / [[8540, 1, 1], [2, 7490, 0], [0, 1, 5053]], 
* Val   accuracy / confusion: 99.20% / [[1063, 4, 2], [9, 926, 3], [2, 1, 630]]

------------------------------ Epoch 198 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000193
- Iter 262 / 659, Loss: 0.000058
- Iter 393 / 659, Loss: 0.000000
- Iter 524 / 659, Loss: 0.000023
- Iter 655 / 659, Loss: 0.000001

* Train accuracy / confusion: 99.98% / [[8542, 0, 0], [1, 7487, 2], [0, 1, 5055]], 
* Val   accuracy / confusion: 99.36% / [[1065, 4, 0], [5, 930, 3], [1, 4, 628]]

------------------------------ Epoch 199 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000002
- Iter 262 / 659, Loss: 0.000047
- Iter 393 / 659, Loss: 0.000858
- Iter 524 / 659, Loss: 0.000005
- Iter 655 / 659, Loss: 0.000034

* Train accuracy / confusion: 99.99% / [[8543, 1, 0], [1, 7490, 0], [0, 0, 5053]], 
* Val   accuracy / confusion: 99.39% / [[1065, 4, 0], [5, 932, 1], [2, 4, 627]]

------------------------------ Epoch 200 / 200, Learning rate: 5.01e-05 ------------------------------
- Iter 131 / 659, Loss: 0.000025
- Iter 262 / 659, Loss: 0.000008
- Iter 393 / 659, Loss: 0.000002
- Iter 524 / 659, Loss: 0.000010
- Iter 655 / 659, Loss: 0.000056

* Train accuracy / confusion: 100.00% / [[8541, 0, 1], [0, 7493, 0], [0, 0, 5053]], 
* Val   accuracy / confusion: 99.47% / [[1067, 2, 0], [7, 929, 2], [2, 1, 630]]

**************************************** Training Ends ****************************************
- Test accuracy: 99.06%

- Confusion matrix:
 [[31864   190    16]
 [  269 27723   118]
 [   60    94 18806]]

print('- Debug table:')
pprint.pp(test_debug, indent=2, width=100)

- Debug table:
{ '14546': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00865039_170816'},
  '00719': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00164098_041214'},
  '30664': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01303196_020317'},
  '04086': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00498791_260916'},
  '05765': {'GT': 2, 'Acc': ' 73.33%', 'Pred': [0, 8, 22], 'edfname': '00623681_070415'},
  '19208': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01008809_021017'},
  '19516': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01019016_241115'},
  '36174': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01430323_060819'},
  '12726': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00796686_020819'},
  '31736': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01324136_201217'},
  '07979': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00673505_020419'},
  '18204': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00971724_030414'},
  '10785': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00740750_110315'},
  '17786': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00958455_200917'},
  '06324': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00634467_180717'},
  '12220': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00776486_121016'},
  '13629': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00824216_080416'},
  '04527': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00601765_231118'},
  '23994': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01154834_180417'},
  '03080': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00406530_310719'},
  '30733': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01303263_281116'},
  '23424': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01147097_121119'},
  '24505': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01164481_220615'},
  '18902': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00988618_270618'},
  '32089': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01334787_211117'},
  '26285': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01212635_270515'},
  '16435': {'GT': 2, 'Acc': ' 63.33%', 'Pred': [5, 6, 19], 'edfname': '00926040_061115'},
  '19711': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01026841_270116'},
  '02693': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00388436_200616'},
  '06604': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00642740_120918'},
  '28610': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01266829_070416'},
  '17149': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00951066_131217'},
  '25105': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01186214'},
  '03473': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00430821_100217'},
  '30539': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01300391_251116'},
  '27284': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01240949_191015'},
  '31301': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01312293_120417'},
  '25411': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01195277_151119'},
  '16248': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00916546_281114'},
  '23430': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01147097_121119'},
  '07382': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00659706_220218'},
  '19978': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01035046_020715'},
  '32422': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01338710_261017'},
  '15256': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00888613_060819'},
  '23265': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01145402_161117'},
  '18116': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00968332_310518'},
  '14219': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00851302_261217'},
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  '13340': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00816641_270214'},
  '27007': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '01235271_170915'},
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  '16356': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00919146_120517'},
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  '02661': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00385518_010715'},
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  '19603': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01025734_250815'},
  '26723': {'GT': 0, 'Acc': ' 80.00%', 'Pred': [24, 6, 0], 'edfname': '01225382_080715'},
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  '10838': {'GT': 1, 'Acc': ' 63.33%', 'Pred': [11, 19, 0], 'edfname': '00741780_281116'},
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  '14737': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00873142_220319'},
  '16883': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00945146_011214'},
  '17306': {'GT': 0, 'Acc': ' 70.00%', 'Pred': [21, 8, 1], 'edfname': '00952475_210518'},
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  '16145': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00912302_100415'},
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  '27027': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01235272_051115'},
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  '04535': {'GT': 2, 'Acc': ' 43.33%', 'Pred': [17, 0, 13], 'edfname': '00601765_231118'},
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  '36190': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01430323_060819'},
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  '17212': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00951808_251116'},
  '16200': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00916265_090818'},
  '07375': {'GT': 1, 'Acc': ' 93.33%', 'Pred': [0, 28, 2], 'edfname': '00659706_220218'},
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  '08528': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00684846_030717'},
  '03621': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00456090_140417'},
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  '12464': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00784417_100315'},
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  '01125': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00232310_261218'},
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  '22621': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01139924_060417'},
  '03089': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00406530_310719'},
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  '01926': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00313383_180314'},
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  '01483': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00265504_101016'},
  '03669': {'GT': 0, 'Acc': ' 73.33%', 'Pred': [22, 8, 0], 'edfname': '00458897_141016'},
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  '08126': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00675516_090120'},
  '30140': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01296344_211116'},
  '30504': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01300390_251116'},
  '22392': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01137650_290819'},
  '00502': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00138259_030518'},
  '09043': {'GT': 0, 'Acc': ' 80.00%', 'Pred': [24, 6, 0], 'edfname': '00697249_090414'},
  '12495': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00784418_201016'},
  '05155': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00612060_221118'},
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  '10290': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00731508_280817'},
  '04992': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00608836_270219'},
  '12108': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00773108_250615'},
  '27481': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01242661_271015'},
  '22835': {'GT': 0, 'Acc': ' 86.67%', 'Pred': [26, 4, 0], 'edfname': '01139944_040214'},
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  '30385': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01298381_281016'},
  '10833': {'GT': 1, 'Acc': ' 63.33%', 'Pred': [11, 19, 0], 'edfname': '00741780_281116'},
  '06517': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00641692_170315'},
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  '23087': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01141787_190515'},
  '12915': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00803113_260516'},
  '24383': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01159816_310315'},
  '11623': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00760780_141118'},
  '29390': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01280762_171116'},
  '14452': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00861148_300119'},
  '15283': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00888613_080618'},
  '05833': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00626173_030118'},
  '27683': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01246499_301115'},
  '20294': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01054491_080116'},
  '02020': {'GT': 0, 'Acc': ' 83.33%', 'Pred': [25, 5, 0], 'edfname': '00316358_050815'},
  '16265': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00916613_240813'},
  '17901': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00962086_240216'},
  '26957': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01235034_260220'},
  '16165': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00915077_310818'},
  '31028': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01306737_151216'},
  '27369': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01241947_261115'},
  '23951': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01154528_150514'},
  '07677': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00666711_150316'},
  '01180': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00233423_111218'},
  '25840': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01203444_090819'},
  '20720': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01073369_130514'},
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  '18961': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00996941_180517'},
  '14758': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00875320_070415'},
  '32944': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01348263_210917'},
  '03069': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00406530_140819'},
  '10772': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00740694_110315'},
  '13899': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00840267_160317'},
  '23639': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01149073_170414'},
  '28671': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01269786_071019'},
  '13845': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00839295_150318'},
  '05040': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00609184_070618'},
  '16542': {'GT': 0, 'Acc': ' 96.67%', 'Pred': [29, 1, 0], 'edfname': '00927352_070316'},
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  '21024': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01090903_250917'},
  '08375': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00678916_020718'},
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  '06079': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00628699_210519'},
  '21982': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01133832_010716'},
  '30951': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01306520_200117'},
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  '22693': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01139924_291214'},
  '03281': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '00417769_100117'},
  '25208': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01189271_031215'},
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  '34537': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01386864_220618'},
  '35086': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01403151_080319'},
  '20277': {'GT': 2, 'Acc': ' 96.67%', 'Pred': [1, 0, 29], 'edfname': '01054491_080116'},
  '11691': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00762439_081217'},
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  '11215': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00752398_110214'},
  '30465': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '01299709_090419'},
  '29264': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01279168_171219'},
  '19712': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01026990_241215'},
  '27249': {'GT': 0, 'Acc': '100.00%', 'Pred': [30, 0, 0], 'edfname': '01240162_121015'},
  '04817': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '00606315_300715'},
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  '10173': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00725683_091115'},
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  '16072': {'GT': 1, 'Acc': '100.00%', 'Pred': [0, 30, 0], 'edfname': '00907971_030217'},
  '30114': {'GT': 1, 'Acc': ' 90.00%', 'Pred': [3, 27, 0], 'edfname': '01296342_141116'},
  '22673': {'GT': 2, 'Acc': '100.00%', 'Pred': [0, 0, 30], 'edfname': '01139924_140717'},
  '22261': {'GT': 1, 'Acc': '  0.00%', 'Pred': [30, 0, 0], 'edfname': '01135545_230715'},
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